{
    "cells": [
        {
            "cell_type": "code",
            "execution_count": 110,
            "metadata": {
                "collapsed": false
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "The autoreload extension is already loaded. To reload it, use:\n",
                        "  %reload_ext autoreload\n"
                    ]
                }
            ],
            "source": [
                "%load_ext autoreload\n",
                "%autoreload 2"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 111,
            "metadata": {
                "collapsed": true
            },
            "outputs": [],
            "source": [
                "import numpy as np\n",
                "import pandas as pd\n",
                "import matplotlib.pyplot as plt\n",
                "from sklearn.linear_model import LinearRegression, Lasso, LogisticRegression\n",
                "from sklearn.ensemble import GradientBoostingRegressor, GradientBoostingClassifier\n",
                "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n",
                "from sklearn.preprocessing import PolynomialFeatures, StandardScaler\n",
                "from sklearn.pipeline import Pipeline\n",
                "import scipy.special"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# NLSYM DATA"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 112,
            "metadata": {
                "collapsed": false
            },
            "outputs": [
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": [
                        "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\preprocessing\\data.py:645: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
                        "  return self.partial_fit(X, y)\n",
                        "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:464: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n",
                        "  return self.fit(X, **fit_params).transform(X)\n"
                    ]
                }
            ],
            "source": [
                "# Preprocess data\n",
                "df = pd.read_csv(\"data/card.csv\")\n",
                "data_filter = df['educ'].values >= 6\n",
                "T = df['educ'].values[data_filter]\n",
                "Z = df['nearc4'].values[data_filter]\n",
                "y = df['lwage'].values[data_filter]\n",
                "\n",
                "# Impute missing values with mean, add dummy columns\n",
                "# I excluded the columns 'weights' as we don't know what it is\n",
                "X_df = df[['exper', 'expersq']].copy()\n",
                "X_df['fatheduc'] = df['fatheduc'].fillna(value=df['fatheduc'].mean())\n",
                "X_df['fatheduc_nan'] = df['fatheduc'].isnull()*1\n",
                "X_df['motheduc'] = df['motheduc'].fillna(value=df['motheduc'].mean())\n",
                "X_df['motheduc_nan'] = df['motheduc'].isnull()*1\n",
                "X_df[['momdad14', 'sinmom14', 'reg661', 'reg662',\n",
                "        'reg663', 'reg664', 'reg665', 'reg666', 'reg667', 'reg668', 'reg669', 'south66']] = df[['momdad14', 'sinmom14', \n",
                "        'reg661', 'reg662','reg663', 'reg664', 'reg665', 'reg666', 'reg667', 'reg668', 'reg669', 'south66']]\n",
                "X_df[['black', 'smsa', 'south', 'smsa66']] = df[['black', 'smsa', 'south', 'smsa66']]\n",
                "columns_to_scale = ['fatheduc', 'motheduc', 'exper', 'expersq']\n",
                "scaler = StandardScaler()\n",
                "X_df[columns_to_scale] = scaler.fit_transform(X_df[columns_to_scale])\n",
                "X = X_df.values[data_filter]\n",
                "\n",
                "true_fn = lambda x: np.zeros(x.shape[0])"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 113,
            "metadata": {
                "collapsed": false
            },
            "outputs": [
                {
                    "data": {
                        "text/plain": [
                            "Index(['exper', 'expersq', 'fatheduc', 'fatheduc_nan', 'motheduc',\n",
                            "       'motheduc_nan', 'momdad14', 'sinmom14', 'reg661', 'reg662', 'reg663',\n",
                            "       'reg664', 'reg665', 'reg666', 'reg667', 'reg668', 'reg669', 'south66',\n",
                            "       'black', 'smsa', 'south', 'smsa66'],\n",
                            "      dtype='object')"
                        ]
                    },
                    "execution_count": 113,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "X_df.columns"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# ANALYSIS"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "### Defining some hyperparameters"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 114,
            "metadata": {
                "collapsed": false
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "0.006686726847208292\n"
                    ]
                }
            ],
            "source": [
                "random_seed = 123459 # random seed for each experiment\n",
                "N_SPLITS = 10 # number of splits for cross-fitting\n",
                "COV_CLIP = 20/(X.shape[0]) # covariance clipping in driv\n",
                "print(COV_CLIP)"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "### Defining some generic non-parametric regressors and classifiers"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 115,
            "metadata": {
                "collapsed": false
            },
            "outputs": [],
            "source": [
                "from utilities import RegWrapper\n",
                "from sklearn.model_selection import GridSearchCV\n",
                "from sklearn.linear_model import LassoCV, LogisticRegressionCV\n",
                "from xgboost import XGBClassifier, XGBRegressor\n",
                "from xgb_utilities import XGBWrapper\n",
                "\n",
                "# Linear models for regression and classification \n",
                "model = lambda: LassoCV(cv=5, n_jobs=-1)\n",
                "model_clf = lambda: RegWrapper(LogisticRegressionCV(cv=5, solver='liblinear', scoring='neg_log_loss', n_jobs=-1))"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "### Some utility functions"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 116,
            "metadata": {
                "collapsed": true
            },
            "outputs": [],
            "source": [
                "def nuisance_diagnostic(cate, nuisance_model, property_name, property_fn, \n",
                "                        index_names=None, statistic=np.std, threshold=None):\n",
                "    std = statistic([property_fn(ns) for ns in cate.fitted_nuisances[nuisance_model]], axis=0)\n",
                "    if hasattr(std, '__len__'):\n",
                "        if threshold is None:\n",
                "            coefs = np.argmax(std).flatten()\n",
                "        else:\n",
                "            coefs = np.argwhere(std >= threshold).flatten()\n",
                "        if index_names is None:\n",
                "            index_names = np.arange(std.shape[0])\n",
                "        for high_var in coefs:\n",
                "            plt.figure(figsize=(4,3))\n",
                "            plt.title(\"{}: {}[{}] Across Folds\".format(nuisance_model, property_name, index_names[high_var]))\n",
                "            plt.plot([property_fn(ns)[high_var] for ns in cate.fitted_nuisances[nuisance_model]])\n",
                "            plt.xlabel('fold')\n",
                "            plt.ylabel('property')\n",
                "            plt.show()\n",
                "    else:\n",
                "        plt.figure(figsize=(4,3))\n",
                "        plt.title(\"{}: {} Across Folds\".format(nuisance_model, property_name))    \n",
                "        plt.plot([property_fn(ns) for ns in cate.fitted_nuisances[nuisance_model]])\n",
                "        plt.xlabel('fold')\n",
                "        plt.ylabel('property')\n",
                "        plt.show()\n",
                "    "
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# ATE via DMLATEIV"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 117,
            "metadata": {
                "collapsed": true
            },
            "outputs": [],
            "source": [
                "from dml_ate_iv import DMLATEIV\n",
                "\n",
                "np.random.seed(random_seed)\n",
                "\n",
                "# We need to specify models to be used for each of these residualizations\n",
                "model_Y_X = lambda: model() # model for E[Y | X]\n",
                "model_T_X = lambda: model() # model for E[T | X]. We use a regressor since T is continuous\n",
                "model_Z_X = lambda: model_clf() # model for E[Z | X]. We use a classifier since Z is binary\n",
                "\n",
                "dmlate = DMLATEIV(model_Y_X(), model_T_X(), model_Z_X(),\n",
                "                  n_splits=N_SPLITS, # n_splits determines the number of splits to be used for cross-fitting.\n",
                "                  binary_instrument=True, # a flag whether to stratify cross-fitting by instrument\n",
                "                  binary_treatment=True # a flag whether to stratify cross-fitting by treatment\n",
                "                 )"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 118,
            "metadata": {
                "collapsed": false
            },
            "outputs": [
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": [
                        "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:652: Warning: The least populated class in y has only 8 members, which is too few. The minimum number of members in any class cannot be less than n_splits=10.\n",
                        "  % (min_groups, self.n_splits)), Warning)\n"
                    ]
                },
                {
                    "data": {
                        "text/plain": [
                            "<dml_ate_iv.DMLATEIV at 0x222d55666a0>"
                        ]
                    },
                    "execution_count": 118,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "# We fit DMLATEIV with these models\n",
                "dmlate.fit(y, T, X, Z)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 119,
            "metadata": {
                "collapsed": true
            },
            "outputs": [],
            "source": [
                "# We call effect() to get the ATE\n",
                "ta_effect = dmlate.effect()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 120,
            "metadata": {
                "collapsed": false
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "ATE Estimate: 0.137\n",
                        "Standard error: 0.056\n"
                    ]
                }
            ],
            "source": [
                "# Comparison with true ATE\n",
                "print(\"ATE Estimate: {:.3f}\".format(ta_effect))\n",
                "print(\"Standard error: {:.3f}\".format(dmlate.std))"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 121,
            "metadata": {
                "collapsed": false
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "ATE Estimate Interval: (0.027, 0.248)\n"
                    ]
                }
            ],
            "source": [
                "# We can call normal_effect_interval to get confidence intervals based\n",
                "# based on the asympotic normal approximation\n",
                "lower, upper = dmlate.normal_effect_interval(lower=2.5, upper=97.5)\n",
                "# Comparison with true ATE\n",
                "print(\"ATE Estimate Interval: ({:.3f}, {:.3f})\".format(lower, upper))"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# ATE and CATE via DMLIV"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 122,
            "metadata": {
                "collapsed": true
            },
            "outputs": [],
            "source": [
                "from dml_iv import DMLIV\n",
                "from utilities import SelectiveLasso, SeparateModel\n",
                "from sklearn.linear_model import LassoCV, LogisticRegressionCV\n",
                "from econml.utilities import hstack\n",
                "\n",
                "np.random.seed(random_seed)\n",
                "\n",
                "# For DMLIV we also need a model for E[T | X, Z]. To allow for heterogeneity in the compliance, i.e.\n",
                "# T = beta(X)*Z + gamma(X)\n",
                "# we train a separate model for Z=1 and Z=0. The model for Z=1 learns the\n",
                "# quantity beta(X) + gamma(X) and the model for Z=0 learns gamma(X).\n",
                "model_T_XZ = lambda: SeparateModel(model(), model())\n",
                "\n",
                "# We now specify the features to be used for heterogeneity. We will fit a CATE model of the form\n",
                "#      theta(X) = <theta, phi(X)>\n",
                "# for some set of features phi(X). The featurizer needs to support fit_transform, that takes\n",
                "# X and returns phi(X). We need to include a bias if we also want a constant term.\n",
                "dmliv_featurizer = lambda: PolynomialFeatures(degree=1, include_bias=True)\n",
                "\n",
                "# Then we need to specify a model to be used for fitting the parameters theta in the linear form.\n",
                "# This model will minimize the square loss:\n",
                "#        (Y - E[Y|X] - <theta, phi(X)> * (E[T|X,Z] - E[T|X]))**2\n",
                "dmliv_model_effect = lambda: LinearRegression(fit_intercept=False)\n",
                "\n",
                "\n",
                "# Potentially with some regularization on theta. Here we use an ell_1 penalty on theta\n",
                "# If we also have a prior that there is no effect heterogeneity we can use a selective lasso\n",
                "# that does not penalize the constant term in the CATE model\n",
                "#dmliv_model_effect = lambda: SelectiveLasso(np.arange(1, X.shape[1]+1), LassoCV(cv=5, fit_intercept=False))\n",
                "\n",
                "\n",
                "# We initialize DMLIV with all these models and call fit\n",
                "cate = DMLIV(model_Y_X(), model_T_X(), model_T_XZ(), \n",
                "             dmliv_model_effect(), dmliv_featurizer(),\n",
                "             n_splits=N_SPLITS, # number of splits to use for cross-fitting\n",
                "             binary_instrument=True, # a flag whether to stratify cross-fitting by instrument\n",
                "             binary_treatment=True # a flag whether to stratify cross-fitting by treatment\n",
                "            )"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 123,
            "metadata": {
                "collapsed": false
            },
            "outputs": [
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": [
                        "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:652: Warning: The least populated class in y has only 8 members, which is too few. The minimum number of members in any class cannot be less than n_splits=10.\n",
                        "  % (min_groups, self.n_splits)), Warning)\n"
                    ]
                },
                {
                    "data": {
                        "text/plain": [
                            "<dml_iv.DMLIV at 0x222d55126a0>"
                        ]
                    },
                    "execution_count": 123,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "cate.fit(y, T, X, Z)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 124,
            "metadata": {
                "collapsed": false
            },
            "outputs": [
                {
                    "data": {
                        "image/png": 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                        "text/plain": [
                            "<Figure size 576x396 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "# To get the CATE at every X we call effect(X)\n",
                "dml_effect = cate.effect(X)\n",
                "plt.hist(dml_effect, label='est')\n",
                "plt.legend()\n",
                "plt.show()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 125,
            "metadata": {
                "collapsed": false
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "[-0.05707952 -0.1101906   0.12368182 -0.01630396  0.01638841  0.0206494\n",
                        "  0.14824186  0.04105756  0.10153722  0.15699125 -0.03017467  0.04778027\n",
                        "  0.08408015 -0.01652653 -0.00710907  0.05880703 -0.19192102 -0.15900693\n",
                        "  0.03517143 -0.16334309  0.02435382  0.05080503  0.07929768]\n",
                        "NA\n"
                    ]
                }
            ],
            "source": [
                "# To get the parameter theta we call coef_. The first entry is the intercept of the CATE model\n",
                "print(cate.coef_)\n",
                "try:\n",
                "    print(cate.effect_model.lasso_model.alpha_)\n",
                "    plt.plot(cate.effect_model.lasso_model.alphas_, cate.effect_model.lasso_model.mse_path_)\n",
                "    plt.show()\n",
                "except:\n",
                "    print(\"NA\")"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 126,
            "metadata": {
                "collapsed": false
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "ATE Estimate: 0.072\n"
                    ]
                }
            ],
            "source": [
                "# We can average the CATE to get an ATE\n",
                "print(\"ATE Estimate: {:.3f}\".format(np.mean(dml_effect)))"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 127,
            "metadata": {
                "collapsed": false
            },
            "outputs": [
                {
                    "data": {
                        "image/png": 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rUef3kNd97MtvLlz4ltq376Df/36ykpJqlJ9foMrKCj399LPKzMzS228vVFZWJ1188RBl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\n",
                        "text/plain": [
                            "<Figure size 576x396 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "# We can also see how it compares to the true CATE at each target point and calculate MSE\n",
                "plt.title(\"DMLIV CATE as Function of {}\".format(X_df.columns[np.argmax(np.abs(cate.coef_[1:]))]))\n",
                "plt.scatter(X[:, np.argmax(np.abs(cate.coef_[1:]))], dml_effect, label='est')\n",
                "plt.scatter(X[:, np.argmax(np.abs(cate.coef_[1:]))], true_fn(X), label='true', alpha=.2)\n",
                "plt.legend()\n",
                "plt.show()"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "#### Some Diagnostics of the Fitted Nuisance Models Across Folds"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 128,
            "metadata": {
                "collapsed": false
            },
            "outputs": [
                {
                    "data": {
                        "image/png": 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PK68cxa5duwgLCyM8PFy/X0ldFuTvgRCw/OdT/3RpOtTK1HV990aOk9QZF1Ov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\n",
                        "text/plain": [
                            "<Figure size 288x216 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "image/png": 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s27atxefZk6tWQ3SoL2kXypxuyRB7KdVVk1tYQWy4X6OL3NlKsJ873h5aUh2k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\nMklJkuTQZJKSJMmhySQlSZJD+/9D7I/B3DHIIAAAAABJRU5ErkJggg==\n",
                        "text/plain": [
                            "<Figure size 288x216 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "image/png": 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6tdlzOqJfLz6AowMfA55yMXcoFNVmJk04EolEZ64Pi8WCUqkEm82GRCLRqaUiEAggkUha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\nCYeiKJOhCYeiKJOhCYeiKJP5f0FC+5y71xFQAAAAAElFTkSuQmCC\n",
                        "text/plain": [
                            "<Figure size 288x216 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "# The property .fitted_nuisances is a dictionary of the form:\n",
                "# {'name_of_nuisance': [fitted_model_fold_1, fitted_model_fold_2, ...]}\n",
                "# then we can access all properties of each of the fitted models for each fold.\n",
                "# If for instance all nuisances have a linear form we can look at the standard deviation\n",
                "# of the coefficients of each of the nuisance model across folds to check for stability\n",
                "try:\n",
                "    nuisance_diagnostic(cate, 'model_T_XZ', 'coef', lambda ns: ns.coef_.flatten(),\n",
                "                        [c+\"_0\" for c in X_df.columns] + [c+\"_1\" for c in X_df.columns])\n",
                "    nuisance_diagnostic(cate, 'model_T_X', 'coef', lambda ns: ns.coef_.flatten(), X_df.columns)\n",
                "    nuisance_diagnostic(cate, 'model_Y_X', 'coef', lambda ns: ns.coef_.flatten(), X_df.columns)\n",
                "except:\n",
                "    print(\"Unavailable\")"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 129,
            "metadata": {
                "collapsed": false,
                "scrolled": false
            },
            "outputs": [
                {
                    "data": {
                        "image/png": 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9ELymgVtZSWitmlWXvFAuZyrJaOLAJGgLIUQvKk/38rsotjSTCQwGwFm0pnsl\ngzUwbJj1nLZL3IbhBjoyyCUZTbwfCdpCCNGLyj1tr51iPE6rvwYAX9YK2qvdlaz3BwCojCbJuzLY\nMj6KekcGeUdPW5LRRFcStIUQohe197Sr2+ZoJx3W0Hcw0dbTrq1hW2UIgIpYlKwngb3oJFLtL6/2\nJT1t8X4kaAshRC+KthVW8eWtpLJMyU3BYRBKpClpOunKAFFvJUW7k3AuRs5tzd1uDNaVe9o+j4MK\nt51GWe1L7EOCthBC9KJoMofHZUNrjVLSbJQKDgx3imA2S8JbjdJ10DRyoTqChQTKZn0Nt/rryEci\n5WledUEvkViWkmn25+GIAUaCthBC9KJooq2wSnMzGUcVoGE6krjMIq1t5UwBjLrB6CiqUtbXcE6r\nRBkGpdY4YC0cUjIVLa25/jgMMUBJ0BZCiF6SNYpkjSKB9ulezioA7MoaKo9WBnG39ayzg4cA4I/l\nKNryKMOHAvIN+y4cIsloooMEbSGE6CXRfQqrtM/R9uasoN0aqmFCdQUaEAnVAuCPxshVJHAaFbR6\nXR1LdMpcbXEAErSFEKKXxPYprJLyhwGoSlkTslO1tVSlllJlL7KtIoBCI5yLknVZyWh7gzUU9p2r\nLcloohMJ2kII0UvKPW2PnVI8TspZRUkvEkxYgTcRrKG6uIMqs5mEplOoDhHOx8jbrbW1W3yhck+7\nNmANj0tPW3QmQVsIIXpJubAKGUw0cqYHw5MilDJIO7wUnC6CxKlW1pKdqn4IbrOAO28tIpJxhso9\nbbfTTsDvkqAtupCgLYQQvaS9p+03kuQcPkCn4EpRZRjEfQGqbUWcWpFqzRouLw4ZCkBlSwFTK1Ey\nq63VvtqmedUFPLQkDPKFUr8cjxh4JGgLIUQvab+m7cklSDusJDS9LUDHKoPU2KyEtEDbfa3hOgD8\nsVZyniR2w0dOlSjGokDHde1ITDLIhUWCthBC9JJo0qDCbYdYtDzdy11sBaA1FCaomtFtHmrdGgB7\nqqy65DW5GDlPCl3Z2BMKlRcOqZNypmIf9u5sZJomc+fOZcOGDTidTm699VZGjBgBQFNTE9dee235\nuevWreN73/sel156KRdccAF+v1WHd+jQodxxxx29cAhCCNH/lFJEEwa1AQ+F5mYybUHbn7F61clw\nmNGlPTgq6ghU1uBJZ9ll83CK20utESVrt9bObqpsyyCfeELHtC/JIBdtuhW0X3zxRfL5PE888QQr\nVqxg/vz5LFy4EIBwOMxjjz0GwDvvvMO9997Ll770JQzDutbT/pgQQnyYZIwiRqFE0O+isLmZlGsc\nSjM7FgoJhanR1uD0fAR/YCSRlwUaAAAgAElEQVSBnU3sKbqx1Q8hsHUTKKf1PFewnEE+SFb7Evvo\n1vD4smXLmDZtGgCTJk1i9erV+z1HKcUtt9zC3LlzsdlsrF+/nmw2yxVXXMFll13GihUretZyIYQY\nQNoXCglWusk3N5NxVmO40oQSRQq6jVKlHx8ZHJ5afMExVJMANGzDhwNQEVUoFAUtQKFtta+aKje6\npslcbVHWrZ52KpXC5/OVb9tsNorFInZ7x+5efvlljj/+eEaPHg2A2+1m9uzZXHTRRWzbto2vf/3r\nPPvss122EUKIo1X7dK+QVyebMiiFHRieZgJpg7g3SI3DQDPB4a7F5QkQshegAJnaely0ra0dzmAv\nVJNrXQqA3aYTrnbLtC9R1q2I6fP5SKfT5dumae4XfP/yl79w2WWXlW+PGjWKESNGoGkao0aNorq6\nmqamJurr6w/6WoGAF7vd1p1mvq9w2N+r+xOHT96D/iXnv/cVNjUDMNynSDmtzHFsSRymScwfJGxr\nBVNj8DCrIzMsUAURaK0NUIuVjPaeJ0h1fBB7DIP/CHrRbDaGDapk6bpGPBUufF5nPx3dh8/R+j/Q\nraB96qmnsmTJEs4991xWrFjB2LFj93vOmjVrOPXUU8u3Fy1axMaNG5k7dy6NjY2kUinC4fAHvlas\nl4eFwmE/TU3JXt2nODzyHvQvOf99Y/seK0vcEW8pZ447TOu+RCBETWEvdleAlqhBOOwk5LZW/HrP\nZies26gzYqxx5qgGGiqD7Fm/DWdtLUGfFajXbGpi9ODKI39gH0ID/X/gYD8ounVN+6yzzsLpdHLJ\nJZdwxx13MGfOHBYvXswTTzwBQDQapaKiAk3TyttceOGFJJNJLr30Uq655hpuv/12GRoXQnxoxNoK\nq3iNZNuSnODLW0E7WRMmqCI43LXl59dUD8dBgUhRQwvXEc7HKGCNKsY9IQoR67q2LBwiOutW1NR1\nnXnz5nW5b8yYMeW/g8Egf/7zn7s87nQ6+dnPftadlxNCiAGv/Zq2M9Va7mkHUp2CNutwej5Sfr7D\nVU1Q30RTqRLviOFkGvfgTFpBO2cPkG9spOJEGBRoX6JTgraQ4ipCCNErokmDSq8DM9pC2llN3pkl\nGC8CYA9XYddKODy15LIFCm1lSWucYKJTrA0A4GvOUrTnoFRdrkEuc7VFZxK0hRCih5RSxJIGgUo3\nmeYoebsXw5MimCyQcPkIuKyhc91Rw+O/eovFT6wEoNZbAUCs0rpuHc7FyXpS2AtempojAFT7XTgd\nuvS0BSBBWwgheiyZLVAomgT9LuKtVmWzojOJzygS9wUJqWY03UFLi41susDWzVameX2lVca00WcN\ngdcaUbIuKzhvz1r70TWNuoCXxmgWpdSRPjQxwEjQFkKIHoq1FVYJeW0k81aqkE21LQpSHaK6tBuH\nO0zDLitjOZ00yKQMBvmtqWENNjv4q6jNx8i25e9GNDeqaA2v1wW9GIUS8VT+SB6WGIAkaAshRA+1\nJ6HV6rlyzXFvoW26V6iGEFEcnjr27motb9McSRNwObBhEjN9uIbUUVnMQNYFQNoRoNDcBMCgoNUT\nlwxyIUFbCCF6qH0d7UApXc4cr8rGAcjX1eAlh90VpqFT0G6JpLBpGkEnxKnEVmslnHmbC5h6kZKq\n7ljtK9C22pckox3zJGgLIUQPtfe0/fkkaUc1JVueUMIKsK6wH02DbK6SQr7EkBHWkHhzo7WQSK3H\nQwEH2SprXDyci2O4k9jzflJ79wIyV1t0kKAthBA91N7TdqQTZB0+DE+KQMLEsDnw+6zr0k0RK0N8\n7ImDcLntNEesoF1XYWWQR33WHO1aI0bOmUFDZ0ujrPYlupKgLYQQPRRL5NA0MFozoOnk3CmqU0Xi\nFQECZiM2u489u6ze+OBhVdQNrqQ1mqFQKBF2W8G8qTIIdju1RpS03Qr0u9NW4pnP46DCbZdpX0KC\nthBC9FQ0aVDtc9Eat4Kspiewm4p4ZZCAuQeHp5Y9O1up8LvwV7kZNLgKpSDalKbWYwXtuFaNvS5A\nqNCKUbCS0WJ5R/k1BgW9NMWzlEzzyB+gGDAkaAshRA+YbYVVgn4XrVlrHrW7ZE33SoVqqCZBiSC5\nTIH6YVVomkZd28IfLZEUNW4HGhBTVeg1TuzKxN6ioVDklR+zYP0QqAt6KZmK5tZcvxynGBgkaAsh\nRA8k0nlKpiLk1UmZbgD8OStznEEhbJoimbKuW9cPtTLL24N2c2MKh64TcDmIU4WqsobFw9kkeVcK\nvVCF0bBPOVMZIj+mSdAWQogeiLYVVqmzGaSd1SitRCBl9bSd4bZA22gNgQ8eZmWO1w6yMspb2pLR\nwm4HWVwUgtaweK0RJe9MoZsOdmzbCnQkozVIMtoxTYK2EEL0QPt0r1ApQ8ZRSdGVJpQoYaLhrSoC\nGju2KlxuO4EaK/DaHTYCNRU0R1IopcrXtWMhq6xprREjaysA8N5uqwZ5XUAKrAgJ2kII0SPt071c\n2TSmbifrSRNIlEh4/ARowOYIkojnqR9qXc9uF6qtoFgwaY1lyxnkMWcIrcpNXT5GWlnlUCOJtmva\n7QVWJGgf0yRoCyFED7T3tM2MFbwLzgRewyTuDxJUTeRL1pB4/bCqLtvV1PoAa4i8vaed0GvRgna8\npRyFhHVfKmdlkLucNgJ+lyzReYyToC2EED3Q3tPOZawkMoeySpVmQiHcWp5Ewuoh17ddz25XU2cF\n7eZIqtzTbtVr0EJWDzuYylBwZDHzvvI2g4JeogkDo209bnHskaAthBA9EEvksOkaqbb8MJ9hZY7r\ndQEAGhsd2B16OUi3C7X3tBtTeOw2/A4bUbMCPdyWjJaPU3CmsBc9xJqjQEcGeVNMktGOVRK0hRCi\nB9oLq6SKDsCkOm1ljrvD1vSvPbvs1A2uxGbr+nXr8Tqp8DnL5Uxr3E4SRY1SsGNt7ZzNGnpfv24D\nAIPaktHkuvaxS4K2EEJ0U8k0iacMarw6KZsPbFmCSStxzFNtoHCSzbn2GxpvF6rzkU7myWby1Lqd\nKCAdGgUOnbp8jFTbV/SuPdYSneW52nJd+5glQVsIIbqpNZVHKRikGxRtbgx3hkCyRNbuosoeIV+s\nArRyUZV9dSSjpQm3JaOlXKPQahwE8wnShtVbj8at6+Udc7UlaB+rJGgLIUQ3tRdWqSxYw9hZT4qq\nZIm4P0illqK11YOua9QNqTzg9uVktMYUtW3JaHFbGD3kQkfhSxQo6QXyGeuxUJUbm67Jal/HMAna\nQgjRTdGkFawdhlUIRdlasSnIBYLomqIp4iI8yI/DYTvg9qFO077ae9oxswKtxvq71mgl70piMyow\n8gXsNp2aao/0tI9hErSFEKKb2nvaJcNaectTtDLHtbDVs25NePebn91ZZbUHu0OnOZKi0mHDpes0\nGwpHfRiwKqPl7Vk0NLbu2gNYyWipbIFUttBnxyUGLgnaQgjRTe2FVYyCVemsKhsDwFNrFURJpire\n93o2gK5rhGp9xFsylEomYY+DFiOPe8R4AOryMTJYK4e9t80K2pKMdmyToC2EEN3UXlglXXKgqwzB\nlHXbW5UlX/BQLNoZdJCgDVYymmkqYs0Zat1OSgqy/uPQqh3U5aPES1YyWmSP9YNgkKz2dUyToC2E\nEN0UTeRw6jo5zY3pyBBoLVLSNCor4sTjXoLhCtwex0H30X5du7mx47p2q16HVuPEWSpgS4PSTNJx\nawi+Tlb7OqbZu7ORaZrMnTuXDRs24HQ6ufXWWxkxYkT58f/93/9l0aJFBINBAG6++WZGjhx50G2E\nEOJoE00aDHJrkFFk3VlroRBvJfX2Eonkwa9nt2vPIG+JpKgd7rf+LuiMrAuQ35wmnEmTDzlxZCow\nTSU97WNct4L2iy++SD6f54knnmDFihXMnz+fhQsXlh9fs2YNd955JyeeeGL5vueff/6g2wghxNGk\nUDRJpPOM9SnAhuFK4y4oYrVWZyWRrOCECR8ctIPhCjTNqkE+vq2n3ZTLM374SPKv7qLOiNJid+LK\nVdIQiVJfF8Tp0CVoH6O6NTy+bNkypk2bBsCkSZNYvXp1l8fXrFnDL3/5Sy699FJ+8YtfHNI2Qghx\nNIm1Xb+uKFmLd9i0rpnjyWTF+1ZC68zhsFEV9NISSVHttGPTNCLZPN7RVqenLh8jq1mvsXnnbjRN\noy7gpSGWQSnV68clBrZuBe1UKoXP11H83mazUSwWy7c/97nPMXfuXH7zm9+wbNkylixZ8oHbCCHE\n0STWljmuF61rzb5CCwCeGh3T1LA5A/j8rkPaV02tj7xRIpMwqHE7aMrl8QyeCC6d+kKUVmX1wPfu\n7Vg4JF8wiafyvX1YYoDr1vC4z+cjnU6Xb5umid1u7UopxeWXX47fb12bOeOMM1i7du1BtzmYQMCL\n3X7gwgTdFQ77e3V/4vDJe9C/5Pz33JodVs+6VNLRzTyVSWvhj4qqNMmUl5Fjag96njs/NnxUkM3r\nIhRyJYZWeWlsiOMOhbHX+fDvSJDPWd+VrU05wmE/o4dWs3R9hJypBux7uW1LM9UBL9Vt1+AHmoF6\n3j5It4L2qaeeypIlSzj33HNZsWIFY8eOLT+WSqU477zz+Nvf/obX6+XNN99k5syZ5HK5993mYGK9\nPBcxHPbT1JTs1X2KwyPvQf+S8987tu2OowE504GdKKGkNYTtrTLYEw0SGFbxvud53/fA47MyzN/b\n1ETVaGt4ff3uKK7BgyjuSBBIGuSrMthjdiKRBH6X1ZHZ8F4L9VXuPjzK7km25vjdz9+gbnAlX5h1\nan83Zz8D/X/gYD8ouhW0zzrrLF599VUuueQSlFLcfvvtLF68mEwmw8UXX8w111zDZZddhtPpZMqU\nKZxxxhmYprnfNkIIcbSKJg1cgEIj78hSHSmScblwe2wkUxVMPITM8XbtC4c0R1IMnlgDQCSbZ+yI\n48i9sZHafJyc044zFSaZzA74hUM2r4+gFDTsTpBszeEfgD8sjlbdCtq6rjNv3rwu940ZM6b89wUX\nXMAFF1zwgdsIIcTRKpYwaA9FqYoCVSmT5rCfIGDkq6hqW/v6UHh9LjwVDloaU0zqlEF+ypiTiPE3\n6vJRNuh+fFjJaGNHjwQG7rSvzWsjHX+vj3DKfwzvx9Z8uEhxFSGE6IZoIkelZmVvm/YUGqAC1rBm\nRfVgNE07rP3V1PpIJgz8gAZEcgXcw8aArjGk2EJCs/pYO3c14fM48HkcNMQGXoGVeDRDc2OKuiGV\n6LrGlnWRD95IHDIJ2kII0Q3RpEFAt4K2u9QMQEVIJ5+3Ex5cd9j7a6+M1tqUIeBy0JTNozsc2Goq\nCWbjZPJWD7wlYiX01gU9NMezFEtmbxxOr2nvZZ94ymCGjAzQ1JCiVeqk9xoJ2kIIcZiMQolUtoBb\ngW6WqDKs6V7+QLZtkZAPnp+9r47KaGnCbgfpYolMsYRr6BBspok7aVK05cnGrCA9KOClZCpaWnO9\nd2A9pJRi07oINrvOyONrOG68tVrZ5nVN/dyyDw8J2kIIcZhibQuFKFPHWUoSSlnLZNpDNtIZX7nX\nfDg6J6PVtl3XjmTzeEaOA6A2l8Bwp9AyToxcoVMN8oHTi22JpIm3ZBgxJoTTZWfU2Bp0m8ZmGSLv\nNRK0hRDiMEUTOZyAQqdgNwgkShR1Dc1nR3eE0fXDu54NUBX0YLPrtDSmCLs7ktHcI6wk30H5Fgx7\nDg2NHXsiA7IGeXtwPm5CLQAut4Nho4JEm9LEmtMH21QcIgnaQghxmGLJjszxtKdEIFEi6fOi6Rq+\n6sHd2qeu64TCFUSb04Sc1rztSDaPa5iVeT2s1Eyy7bfA1l0NndbVHhjJaEopNq+L4HDaGDEmWL6/\nPYBLb7t3SNAWQojDFE3kaJ/QlXNlcRYV+UoPSkHN4O5Pbwq1ra3tzFolnptyeeyVleiVFdTk4qRK\nVjBvbEhQ2zalbKAMjzfuseZkjzq+BrvDxhtrGtjekGTkcSFsdp3N65ukVnovkKAthBCHKZo0cGN1\ne20qBoAnpJPJuqkbXNPt/bYno6WaMvgdNiJZq7a4a+gI3IaBStsxtRKpljwuh41gpYvGAZKZXR4a\nn1hLQzTDLxevZeGfV2Nv63nHWzJEm2SIvKckaHdT6p3l7Lj9Fkpp+RAKcayJJgy8SoFS+PNWZnSg\nRlEoVmOzd/9rNdQpGS3sdhLPF8mXTNzDRwIQTmUw3ClUwkGpZFIX8BJNGKSyhR4fU0+YpmLLuiZc\nbjtDRwZYut4K4JFYltXvRWWIvBdJ0O6mhmefo3XrDlLLlvZ3U4QQR1g0aQ2Pu0ppQjlroRBb0InN\nFe7RfkPhCgCaG1OEO1VGa7+uPagQxXBm0ZROU1MrJ462rh2/ubaxR6/bU3t3xsmk84wZH8Zm01m6\nPkJ7bZmXlu1i+JgQdofO5nURGSLvIQna3bS3IY5LFdj25vL+booQ4ghrbc1h03SUViDYtjymVu3A\nHxjSo/06XXaqAh5aIinCbuv6tRW0hwEwwmwipVvztLfs3MNpJ9Zj0zX+tXJPvwbDTWs7ssYbYxl2\nRFKMzuxhmJZi1XstRFMGI4+rIRHP0dyY6rd2fhhI0O4me8HK2IzvlaIBQhxLskYR8taKXmm3IpAo\nkfI40Jw6NfUjerz/UK0PI1fEb1pd1Ug2j7NuEJrdTtiIkTatFb5274lSVeFk0nE17Iyk2N7YP6tW\nlUom721owutzUj+sujw0Pi6xlVP2Wp2al5ft5rgJ7YVWZIi8JyRod0Mpk6aiaAVtRzaNmRsYUy6E\nEH0v2mm6V8pdwJ8xyfpclEwdj79nw+MANbXWELmesHrwTbkCms2Gc+gwfJkUWcPqgcebrO+daSfX\nA/CvlXt7/NrdsWtbDCNXZMx4a3760vURdGVyfG4P41Lb8ZPnlVV7CA+twumysUWGyHtEgnY3pLfu\nwG1aiR+BQpLYmvX93CIhxJESS+Tw0F48JQGAHtQpmtVoWs+/UkNtGeSZ5jQum05TOYN8GLqpqEqU\nMFxpCnEdpRQnjgoR8Lt4c20DRqHU49c/XO21xo+fWEcknmV7Y4qRmb3UnjoJ/wkncHLLWrJGibfX\nRxh5XA3JhEHjnsQRb+eHhQTtbmhcv7n8tw2TzW+u7MfWCCGOpM49bW/RCljeGhsOV22v7L+9nGk0\nkqbW7aTFyFNSqnxduz4fI+dKoxftJBNZdF1j6kfqyRql8tD0kVIslNi6qRl/lZvaej/L2ofGU9up\n+uQZhD5/AZMSG7Fh8tLy3YxpGyLfIrXIu02CdjfEduwGYE+F9U8abbsthPjwiyZyuAG7mSeQs+Zo\ne4Mu/MGeJaG1q/C7cHvsbRnkDkoKorlCOYN8pIqQsVnFV97baQ2JTzvJGiL/98o9vdKGQ7V9SwuF\nfInjJtSiaRpvrW1AVyYneHN4xo7DM+Y4aseNYVxyG3ua06Q0cLntbFkvQ+TdJUG7G9It1oo+m0ef\nAIAtnUSVjvywlBDiyGtpzeFSgJYnmLEKmzgDTiqqule+dF+aphGq9ZGI5wjarTW0m3J5XEOHAjDI\niJJR1vD8jt3WkqDhag8TRwbYuKv1iFZIa08qO35iLU3xLNsjaUZkGxj0yanl9cRD51/A5Lh1CXHJ\nij2MGltDOpVn787WI9bODxMJ2t2g0taUhYbho0g5vITyrTRv2trPrRJCHAmtLRk0TcNw6ATTeQo2\nDXw2HO7eGR6HjiFyV87qDESyeWzeCuyhEFXpJOmilYzWEumYPjXtJOtHw5HqbeeNItu3RAmEvATD\nFeWh+fHpHVSeNpWtySwxo4Dn+OM5blQtg3LNrNjUTHhYFQCbj/BQ/oeFBO1ucOQNFJAJh2gM1OMv\nZVn/5ur+btYxZ2+6kZZstL+bIY4xmbZlORNuCCSLJP1O0LzYHBW99hrtyWhaa3sGeVsy2rDh2AsF\n7Cmdot0gG+0Y4Tt1bA0VbjuvrtpLsWT2Wlvez9ZNzZSKJsdNbBsaf3cnmjKZNCZIk83Fr9bv4g/v\nNVjH8/kZTG5djwLWNKVwexy8t74J0+z7dn7YSNA+TGY+T0UxS9Jega7ZaKq3rmNF3tvZzy07tuRL\nBX627EEWvvu//d0UcQxRSlHMWDNHCnoaewmK1b3by4aOnnauKYNN0zrVIG9LRssmyXlSaFlrbW0A\nh93GlBMHkcgUeHdLS6+250A6L8PZ3JplezTPiGwDg884nRd3N6OA7akcrfkC3rHjOHWwG28xy7/f\n2c3IsSGymQJ7dsgQ+eGSoH2Y0rt24StliXsq8TVkaBluFVNQyVZJrDiC1rSsJ1vMsTfdSCTT3N/N\nEceIjFHEYVr/5+6CFbT0oB2Pv75XX6c65EW3aUQbU9S4HTTl8iilysloo80GMnYrkO/a0xGgP9k2\nRP6vPh4iz2UL7Noao6bOR3XQy9urrdc7gSjxEWNYF89QLDZhmjneaLRKrNbNmMGkxCYyBZOcy7pW\nL4VWDp8E7cPUsNaa7pWoqGakx01rZS0lTSNkxGncemQzN49lyyId0+zWtMg8eXFkRBPWdC8Nk6qs\nFYycQScOT12vvo7NphOsqSDaZJUzzZuK1nyxHLQHZ5vJaNbQ8tZdHUVVhtb6GFVfyar3Woi1DeP3\nhfc2NGGaiuMnWiMMb72zHU2ZfHTyKF7eG8M006SzT5POLObVvbsA8I4dxydqSmjK5LX1e/BWOHlv\nQxOlIzCU/2EiQfswRbdZH8CEv5qPTqzDnjJprqxlkBFl1bIN/dy6Y4NRyrO6eR1fer6Vz77SKkFb\nHDEt8awVtG1FQoaVBOYNOnF6end4HKwh8lJJ4W/LFG/K5XHU1KC53FRnEmRKVm+1saFroZJpJ9ej\nFLyyqu8qpLXXGh8zvpZoIsf2FAzPNqL9xydYF0+TM6yFlEwVJ2ms57Vd6wAY/flzGZfawe5EkeCQ\nSoxckd3bY33Wzg8jCdqHKdVkFQVIVms8G3uKGoo0DRqKDZM9m3b0c+uODaub1+JK5ghHDUbtMti5\ndxNGKd/fzRLHgMZICh0Nw6ERzGZQgLfaicPd8/Kl+2pPRnNkOjLINV3HNXQorryBkXFi6iWSzV17\n1P8xoQ6nQ+ffK/dg9sElu3TSYM+OOIOGVuGvcvP669aP5pMCJksSeZQyKebfQy/acRQqyRfW8sdN\n/8YoFPGOn8BpldaUtG0x68fGZim0clgkaB+mUsr6dZ212SktGYzb3UzzEGv+ZCkek+vaR8DyyLvU\nNxX4/WeDLD6jmuG7MmyIburvZoljQEtTGoCU00EwlSdZYcPpq0XT7b3+Wu3JaMRzQNcMcg2oS+fI\neZKYSTu5Tutpe1x2Pj6+jubWHBv6oBe7Zb0VZI9vWyP77TV70ZTJiNM+wobWDEZ+JUovMvq9akav\nHw/o5NUO7n7xTyilOOXzZ1JrRFnZlMVT4WTrxiZKRRkiP1QStA+TPW/9A2n5Spx5L5lkmljlIABq\njBg7d0pSVF/KFXOsaVmPL6MTq7Kzs95JXUuB1TJELo6AZNxapCOrFajImWQqHX0yNA4Qals4JNeQ\nRgMibVni7eVMjyvsJe6Loimdfz7X9dJceRGRd3t/iHzzOmut7NHjwzQ3tbIj72J4McrK6hoASpn1\nnLwhw+eWruXTW14hkDsJyLPHsYIn/70B7/gJfMIVw0RDuRR5o8TOrTJ181BJ0D4MqlSiopgjY3Ph\nzHkByDVCseAg7apgSK6Jd5dv/oC9iJ5Y1byOglkk7dKobhqCP1ZL0mtj9a7VMsoh+pzRtna2M29d\n0y0F7L2ehNbO5Xbgr3ITa0wRcNm7LBwCMCQdodGWJ1MR4731zWzpVKzkuCFV1Ie8LNvQRKpTL7yn\nEvEsjXsSDBkRwFvh5LWX3wFg9IhqNiWy5I1NnLyxkU8tS4Gu48jEOfP1HdhtQ0Av8mLzn3ljTSOf\n+txpuEsGa6PWyIUUWjl03Qrapmly0003cfHFFzNr1iy2b9/e5fFnnnmGiy66iEsuuYSbbrqpPIH+\nggsuYNasWcyaNYs5c+b0vPVHWHpvA5WFFC0VVTjzVtB2J6qp1BNE6obgL2XZulnqkPelZZGV2IqK\nlsoAQ7eezLAtk9g8tJLQ7hb2pBv6u3niQ87MFVEoKrNWD1YL2HH0UU8brCHyXKZA0GEnXSyRKZbK\nQTuYiVNsGs7u0e+i9BL/em4jmbQV2DVNY9pJgymWTN5Y03v/F53nZgMsey8GSpGcOAqUYvIb/+D0\nFWnyXg/Oi4diG+Rn6N71nLCzFk05sVW18L+v/ItI1WAm21powY7DpbNtUwvFflih7GjUraD94osv\nks/neeKJJ/je977H/Pnzy4/lcjnuu+8+fvvb3/L444+TSqVYsmQJhmElSzz22GM89thj3HHHHb1z\nBEfQ3nWbsaGI+kMAKL2EpnTsxSTN9dZ17UIsRkmq/PSJbDHLupYN1EUd+Jutuu+6aceVOI5QvMSy\nvVKVTvQd0zSxlxTKrggYcQAcASdOd9/0tKEjGc1rrQ9CJJtHd7lw1NbiLeYg78JlumgYuoFctsi/\nnt1YHnE67cRB2HSNf63c02ujUJvXRdB1jdHjamjcuJWdWhX1ToNdpuLUV/7M5PURWitsVF46Hj1o\nx/bpajSXk6mvLqG2MAk0sI9ewQNPreA/PjEOTZlEjRyFfIkd78kQ+aHoVtBetmwZ06ZNA2DSpEms\nXt3xZel0Onn88cfxeDwAFItFXC4X69ev///svXd8HOd57/ud2V6B7cAueiNBAOy9qZOyJdmSJcuW\nbUmOndiJ47STc869OZ9zHdvJjR077cbXsZO4xXJiy5ZlFatREqvETgIEid47sBXb+8z5Y0FKVKFA\nEKQkCt8P8cESuzPzzuzuPO/7lN9DMpnkc5/7HA899BBtbW2LMPxrS2CwkB0eNRS0c62ewqo6PSsR\nMhW+uK5UkMHx2XdngNc57b5OcnIeZcSBMWrDaMmiNojYvJXEtDpOjp19t4e4xHWM159AAWQ1IrZ0\nFACj3YBCXXTVjmmfi5OmcG8AACAASURBVGur4gUX94VktIpKRMCai5CbriLgGiZviTPU56evo1A/\nbjaoWV1vZ9wXZ3g6esVjCfnjBLxxKmqsaLQqDu8r3MPVVQ42v/I8Kzs7CJoV9N/ZDNowar0bsViF\nbtdyVNkMtxw8jSlagaDMkS1t5T+7s9QTYpQloZXLYUFGOxaLYTQaL/xfoVCQyxWmgqIoYrcXEhIe\neeQREokE27ZtQ6vV8vnPf54f/vCHfO1rX+O///f/fmGb9wvRmcKHKqUqnHu924ekSaIMGomKWvKC\niDvl4+yZpeYhV4PT3jMgCWhj9cjIjCv1pI1aRElJRNmIbmqUeCb+bg9zieuUiYmC5GZcrcKaSJJW\nCdjsngvdrK4GtrkMcjlYSIB9o5xpfWyMYNBBpUJJf8VxFCqBQy/2EZsTVlnMJiJ9513jK5xImQxt\n0xkEOc+63gMs7zyN36zmsVsstFhjKJRGnLWfRm0oQ6qIoV6/Bod3kk19oErrUNqm8eXHUWs1JACJ\n/IU2n0tcmgXVKRiNRuLx126OkiShVCov+v+3v/1thoaG+M53voMgCFRXV1NZWXnhcXFxMT6fj9LS\nS8v/WSx6lErFQob5tjgcpgVtl4sWZqs59CiRsRVL6O0+UhMV6IkRtLko8c/w6oCPLy7wGB8ULvc9\niGXidIX6cPkaUGeMJI3j9PncCKRZr5YoClSBuYujk518csPNV2fQ1xEL/Q58kImEC4YwCRTHM/it\nKmptFQu+lvPZzm43otEqycwkwFFEWJJwOEwompcReAKWxUY5ZmlG9laQtQ8irvCTOWPj8Mv9fOp3\nN3GjzcjP9vRwrMvLH96/Bq1mYaVpsiwz1ONHpVawfnMVw3sPMqGy8rHAYepnhwiYinjsNhUVZhNm\nUaaq+eMUO51olLcx0PpjrB8qZ6h/hBXdJwlL2zixrg/98nbOndqGJR9jRjRQmpUIeuM0r1mcvuTv\nxPv1O7Cgd3Dt2rXs27ePD3/4w7S1tdHQ0HDR81/5yldQq9X8y7/8C6JYWMw/9thj9Pb28tWvfpWZ\nmRlisRgOxzsLEoRCi9sb1uEw4fMtzFWkmOvuJWZNyNoEltLNVHpP0TNRgZDI4HN5cPinyIRCjE/O\nolEt7mTjemEh78HhyRPIWQHLeDl5MceURkKMC8jIBLQytoiSdG45+7pPckvVhqs08uuDK/kOfJCZ\nmltpi8kwChmSRUryWBZ0LS/nPbA5DEyOhTGutjIeTuDzRcmaC97M0rQfp16kd6gOj2OUNvUxbq74\nOAPdPg6+1MuK1W62NJXw9OFhnn91kG0tC9NI901HCfrj1DU6CEeS7N3Txj3TI9Qnxpl2lLKvyUla\nM8VqVR69ZQ1ZoRyfL4osl6HSOpkNdpC8/5NofvB91o20Mm1tYaxmiOKmHoST5fg1AqVA67FRXGXm\nBY3xcnivfwcuNaFYkHv8tttuQ61W88lPfpJvfOMb/MVf/AVPP/00jz76KB0dHRcM9MMPP8yDDz7I\niy++yH333Uc0GuWBBx7gz/7sz/ibv/mbi1bn73VkWcaQTRJTmVBIKtTGGLqiZSyvLEZSZCGkJWgs\nZFS6U356x5bi2ovJae8Z7FM1KPNq/CUDBGZdbG0uYcfKUoYiCnKqFKp4NUr/JNHk1dNcXuKDSyJc\ncFHrU4Vclnzx1c0cP895F3mxKDKbyZHJSygtVgSNBgH4cEkGWRZJTZcjCxCraUOtUXB47wCR2SQ7\nVpYicGVNRF6fNZ4aGcY+0U19YpyJ0iqO1WzBXzJDsShSqzVjKbv9wnaCIGB2bQNk3M4QpzfciCad\nYOfZcXTRItK6SfK1afJSlpRccJFn0u+vsOm1ZkFWUxRFvv71r1/0t9ra2guPu7vfWuji7//+7xdy\nuPcEcV+AomyMMWsVACZjApXWjtnegmjpRfSXEiouJIu4Uz5Ot43QUmN7F0d8/RDLxBmYHqNu6gay\nqhQhbRBRqueOrZVoVAqOdXrxGqK4Zx1YQ5U8d6ad+zcvrbaXWFyyiSw5JVhjBQElofjqZo6fxz6X\nQa7LFjLAfakMHoMWdUkp6ZFh6hPj1Jetom+0HqtzlNOJPh7atpljewPse7aHjzywihVVFjqGQ0wF\n4pTaLq/vtyzL9Hd5UWsUeEr1DH/1byhNhxgoquDwtt1oprqRRInVGjW2irtQKHUXba+3NBGe2k9u\n9iTRrQ8wOTGEe3yI7cdX89KNMWRPB/rplfhzDsokmaE+P8uaSxbn4l2HLImrzJPJrgHUco5ZgxWA\nErsSQRDRFa/A5SqUKghIJPRGPCkfHYNLymiLxRnfORzj9YiyyExZD4mom80rXLgseoqNGj60qYLJ\nmIGMOoEyUc2pkc4loZUlFpV0Kgc5iaxWiTVT8KJp7EZEpfaqH/v8SlsRuziDXFtTA0BmbIz7b64D\nWSQxU0YW6OdpquqsTI7Ocu7UBDtWzSWkLUAhbXoiQiySpqq6mKl/+jaEQ3QZK9mz+S6KhpNEPQMo\ngI3OFvTFy960vSCImFxbQM5Tp/Rx6KaPIBmMLPe2s6KjkqycxbRxgqBcSELrOL6UyHsploz2PAkM\nDAMQ1xTiLZXugvFWKHWsrrUiCxLKmBKfsyCykkmkFlWJ6IPMqf5uiv0ekpoYs7YJpJCD2zZV8NT+\nI+w72c7ujRUUac14LdMIsgJrQGBwKvLOO15iiXkyGyzk1qTUIrZ0HEkAy5w2w9XGajcgigKSvyCh\nej6DXFtVDUDW76XWXcTGRiep8XpEWeBkPExzywQarZKj+wepsRsxaJUcPjtF7jJbYfbPdfQyt79E\nemSYPmM5T7l2oNGCRjVFTJlhuUZLWeVdb7sPo3U1otKIO3mEpN5Ix+57EGWJLb2dWGecBHLTGKoH\niSMzM5MkEEou5FJ9IFgy2vNkdrKgKpQVTEhiDrvztYSOktJV5M1BVHEjvvNx7bSPswOBt9zXEvMn\nko6S7jAgIDBd1Uk+ZqFZr2DP8Cj1P/8Btp98n2gizj07a/BmVWQ0CUwRDy+e6H23h77EdUTQX6iW\nSQoC1kSSsFGBq7j8mhxboRSx2PQkpwqJU69vHAIgxeNkZma494ZaFIKKjK+MuCzTFjrMlp3F5HIS\nB5/vYXOTi0giy5n++XsBJUmiv2sGlZzBNNqOVGzj164bURZrKRlJkK4uaJ7fWHkLCqX+bfcjiErM\nzk0Y5AgedYbTjgoMt9yGIRPhppNJlFkNKecgIXUCBJFHH3mV7FITkbdkyWjPk0w0hoSILJkQDDE0\n+teMtraoniJHwUUeUhZcWZ6Un6Pt4+/KWK8nDrWdwRixkzBFiRf5yQddpJs8WI+/giKfR5tK0PP0\n02xrLqVErMRbMoCAAt/wNMmlhJarzgflxjozJ06STyfRZfOEzQrMhmuz0oaCi1yO51CLAt45D566\n1A1zNeIT//T3WMQst64rIztRCzKcSGUwqV6kZpmVmYkIHrFQzXI5LvLRsyOkkjmckUGKd+xgf/Ey\nEAT0egVVjkFGSOFS62n07HzHfRnt6xEUWqqkHmRg7IbdaCoqKQsPsOmEE1mWSdS2F84tFOUnz3Uv\nhbnegiWjPU9UmRRxdRECIlpjHJX2taxRUVTRVFtQRcoJWiRBxJP2MTSxlEF+JUiSxPDxODIyY/YB\nAHSym5RCZkXnKdArSGl1FB8+QCIS5pO3NuMXc2Q0caxJA6+cXpo0XU1O9Xj50j8c4HjXzLs9lKuO\n31tYaWujBe9Zskh1TTLHz2N3GRGAIkQC6Qx5SUZUqVC7PSCKZH1eRr/51+yu1aFXGMkHSwlIMn3J\nEM0rhtAZVPScnqDeYeDsYIBgJPWOx8xMT9P+m4MA1K/0IBpNdBTXA1CTjJEoG0ACbqi4aV4CM6JC\ng8m+nmq58F0+F0lR+oXfB5WKVaPtVA66yZhCxNVxsupiBk738PTh4YVesuuWJaM9TwzZBBFNIY5t\nK5YRxItrsBtr1pHRR1DGjPhsblzpIMmsTCC8FJtZKK2nhxHjGpLOABn7FFKsCE1NObtGO1BkMihX\nFRFeV4Uqm6H38d/QVGXFnSrF6+5HRKT9+Ni7fQrXLd7ZJD96tou8JHOg7crVtt7rREJJ8goBc6Iw\nQckVq1Bpr111yPlkNG1GQpIhkC6sto1r1sJcr4Oc14v3r/43Xxr8JXcdnmHT2TiD/XFy/lNs3q5H\nysuUpCWQ4dWzl15tpyfGGfnWN5lRlaBTSSy773ZODE8Tj8modQq2NZyjPZNFIyrZ5N407/MwOTZh\nErOUiEGGokkyNgeuTz+ESspy45kJ9HETEdcoCAKrs36eODTE0c6lRkCvZ8loz4NEKExRJkbIUPiS\nlrneXPiuM9egsQcRZQWTReUoZImSdIBX2pc+cAshm8lx6pVRJDGHtyiGIMiQKWNbfALn8YOgFjHv\n3EHNDSuJG83ojr1Cyu/noc1bCJn9pDVxtMkcPUt5BYtONifxvSfOkUznMWiVdI+GCMeu39r4fE4i\nHc+Q1Smw5+cqRax6BOHaiSedL/sSI4V49vm4tv3uj1HzD/+M+8t/gspZKD9T5TLUR6fZfDbOjr0B\n0j8eRfEf36VMGSAVSdOci3Hi1CDS27ieUyPDjH37m3hzRnIKDQ2rK4i1t/NqyUoAavVRQoYQUVlm\nU+kGtJeRQa9QGTDY11LDMDLQEYph3rYd/fr1FKd83HhYTcRSSHyLamx45Cg/eqZrSffidSwZ7Xkw\n0TOEXkoT0VoAqChzv+k1giBSU1X48IY1hdd5Uj5O91z/rsOrQeuxMfIp8JdOElEWDG+zP82mmSGk\nWAJFSxEp5Xo0+g34Ni5Hkc/T/9ijVC+rxOI34vP0IyJwcN/Au3wm1x+/2tfPyHSU7S2lfHR7NbIM\nJ67jfsizc6qMabUCa6agiqZzXf367Nej1akwmDTkfIWxnO+tDaA0mzGuXkPlV76GpqoaOZNhYvMd\nPFa7nqMtBibLdMiSTE3vC2hycXQKIx/tfoa+P/9vTP7Ldwg++1viHefIx2Ik+/sY/7u/RYrHCa+6\nFShojbd29hKMF2Q9bl3exam5lf4Oz5bLPhezcws1QiF0dTYYQxAESh/6HPliE3W+Lhq6i4kbg2RV\nFu5VjyFJ8P8/fpaZRVbHfL+yZLTngbevUDeYVhYhqVOYLW822gAbmjaSVaXI5ouREShL+5gOxJaS\nKS6TWDRN27FRsqoMUWsDgsGLmDRy28nDxDvbQSGQXr6Opx8d4KlfdFO7dQOzxTaUp06SnpriDqOb\nWdskaVWKjD9BILDURGSxONXj5aVT47jtBj59WwMbljsRBDh+HXdoCszEAEgJYEvHSWgEXM7Kaz4O\nu9NIPlAIt51fab8eUavF88d/hsrhxH30GdKmRo7UlvDLHSYSv7cW40MlbGjJIgsiraU3kU5liZ0+\nhf/xx5j4x79j4E+/zNi3voGUTmP/nS8wMStiLtZiVqZ51VpOJpTGbkgh6uKM5PLUFlXhNl6+CIpS\nXYTLVocLH0PRBLFsDoVeT+Xv/zGyANt7e0iqIggIHEXmsxuKiSWz/NOv2okm3nzeHzSWjPY88I9N\nkhXVSOhRGmKodW89yy42VyNb/Yh5NTNFFbjTfrKSQO94+BqP+P3NiUND5HMy4ZpiYqERBFGmcTyF\noNciheOIjWaOnHai1ihIp3KMdBiZ2rwWUZYZ/uUjrF23E21MxFvWgwi89PxS+ddiUIhjd6NWifzB\n3c1o1AqKjBqWV1jonwjjv07zNwZ7CyVSMfIUJVOEzEpcpoprPg67y4gymUfBa7Xab0RpNuP50z9H\naTKxs+cFclNVCAL8MuRDNBsprupieYuFrMrEryvuxfX1v8X9h3+E9Y670De3oPF4cP/Blwlaasll\nJepXuDh1uh1v1gAyrPFMsS+x8FX2ecyurdSIY8gIdIQKkyJ9XT3i7pvQ5hJs6hlBRiam8aAfO8iH\nNlUwE0zwz79uJ539YHcCWzLa8yAbjRNTF1zexqIsgvjW6q+CIFBaUYhzTVoqMWYTmHNx9rcuZTHP\nl4A3Rnf7NFmDglmdBrSFa7e6ZxpZlEGAMety7KVOPvXFTdhdRnrOzlDWvA6foxThbCdSPsfKiTRh\n+wQpRZbAWJjI7PVpUK4VubzEvz55jmQ6x2duW4bH/poU5sbGQhb19egiz6RzjA4EyGkUqCIhRGDW\nrMBRVHPNx2JzFjLIjbKAL5V5Ww+e2uXC/Ud/RkV+lprxHHJGw2w2yZSuDlnKUld5CqVWiQs42BnB\nuGYd9nvupexP/5zKv/wrjGvWXhBUqV5m41WFkbS34JqusvkZlmSMKgOrnS0LPheV1kGTWQ1Au893\n4e/1H3uQUFkxVZEhkKIYYlZ+o/eyu17F5iYXAxMR/vXJDvLSB6PU8K1YMtrzQJVOEpuLU7vsmku+\ndl3LKvJijrDqfPMQH90jS0kU82Xvi30AhOrMJEcmEYv8aNJadFkjxNJQa0Lh2s6dn1iJTq9m5+5C\nh7m+VyIMb9kKwMSj/8F6y3IQYMY5jAAc2rsU274Sfrmvn6GpKNuaS9i+8uJOUeuWOVGIAsc7rz+j\nPdwfIJ+XSTi0FIdfV+6lLrrmY7G7ChMlTSpPRpIJZ95eh0BXU0Pp7/8hN/nbyE9XIAgyj060oS1e\ngZwdZ+u2LAICQ62TZN+wn3Qqx8hgAKvDwODkCEF9EZlQGrc5yl5plqyUY6t7I6q3WbzMF49nEy78\nDCdkYtnCGARRpOoLf0xSLdIQ6Cz8LVPGr048wuc+3EhjpYW2fj8/29P7gQ07LhnteWDIJi6stKs9\nl67NrHE0kioOIMlG4qoiKjI+IrE0qUt8wZYocPzcJP6xMCmrBl+2jYw8i6DIUzFrQjk3V5LXbmXH\nrmYUisJH1+U2s2J1KSF/ArNrGZPuKuT+UUrsbjwzGaJlfSSRGO31L622F8jpXh8vnRyn1KbnM7ve\nrC1t1KloqrYyMhNlOnh9JQsNzMXqo04d1nQIgKxFO6+65MXGXKxDpVbAXF/vt4prvx7jylU0PnAf\nTf0p5LyCVDbDacmIQmXCpHwVtQ1UeZkXn7s4fDTU60PKy9Q2OjgQzZH2JpBlKCkOEVZoEBDYfhll\nXm+HRu9mmS6GjED7zMSFv7vcNXjv2kJJdAhkiWKfh9NFEdoHj/Dlj7VQ4TRyoG3yA1vDvWS034FE\nNEFRNkpYV9AXL/VcOpYlCALF7oKB9hrLceeCyILAKwsQ6n83kWSJ6biX49OneXXiGJJ8dd1RPaEY\nR/cPIQPpuklS43oU1kLmvW3MhGY2glhlov7W+950w9x0Qw1avYqpV6bp2VxQZgq9speG8SwIMG0p\n7Of4oeGreg7XI/7ZJD96pgu1UuRLc3Hst+K8i/x45/VTLZFO5RgdCoJKJCnL2ObKvUT7tV9lQ+He\nYnMayL9Bg/xSFG3fwd3rK2HGDQqJF/oPoXPvAiRuWHeOFDIjXV4mR1/zBp5vw5kqEpnVGVH4Cuc9\nYBkknkuwwrYMm866KOe0xl3oDtnuv1hadfttn6GvXoM1OYUuZUad0PPI8FPsHX+ZL36sAZtZyxOH\nhq6o3ej7lSWj/Q5M9I9iyiVIqIqRdXG0hnfOlmxaUYeMzExRFY6YH4WU59h7+GYmyRLehI+TM208\n3vdb/un09/kfB/+Svzr2d/z03M/5dfuv2Dt26KodvysU49cHB1DFstjrzQxHe5CiFpTFXlRpLatD\nXQBYbt+FKKretL1Wp2LLTbXksxIKZQkjVcuQJoI0aqwocjLx8m6SyPR1zBBeKhuZN7m8xPee7CCR\nzvHpXQ14HMa3fe2aegcqpcixrpnrxm053O9HysskzSpy8SzWbJicCCbXW1ePXAvsTiPK+MXdvt6J\n8o/exeaEDVkWkDPw4lQXRsdG1EKQstpJZODlZ7rIZnIkExnGh0M4Sk0cjkQhk2M2CAZdHLWx4A7f\n4dm8aOfjtFTjUkQYyxqZjb12j9Qpddg/fj/6bEEgqeZcPWJO4tmRl/n2yW+yet0IeoPET5/vuSwt\n9euBJaP9Dkx09pFSmpAFFVpTClGhfsdtVpWvJGEKEVfayItqStIBRr3vjdIvWZbxJ4Oc9rbzRP+z\n/H+t/8b/PPRVvnb02/y44794eewg/bNDFGuLuTlfzcPPydz5opW2vXsYjy7+rPZsMMp/9U5gHggj\nKkWMK8LkxxsQzQFQ5qib1qPxhRFLdVjXfvht97Os2UVpWRHpMz7ObtyJJAiox33UjqdAm2RSUyj7\nOvHKyKKfw/XKY/sHGJqKsKWphO0tpZd8rU6jZGWtjalAgnHf9VFiN9BVSJAK15jJh1PYUnHCJgUe\ny7VPQjuPzWlEmZjz5KXm10VQEAQ+8fDHUQSdZA1ZgvuOkDI1o9TY2VQ3QEqbJBZOc2TfIAPdPmQZ\ndOUmZpVqLIFRJFkkbR8jlU9h1Vposi1ftPMRBIEWixEZkdbx7oue21qxhaFtIoIsUZTW0dS+iS0d\nEmI6x+F4K8ple/A4DvOjp44wOPnB6eq3ZLTfAe/oFPG5JLRiy/wul16lQ+mMAwJ+fRmVeR/ZbJ4x\nb+wqjvTNyLJMMBWizXuWJwee4zut/87/dehr/OWRb/LDcz/jxdH99Ib6MatNrHet5q7qXdxTdwcf\ncm1j69EATY8eo9e0iR7nVoomdvCLJwZ44twY/eE4ydyVl120+iP8YmAa80gcRUZizcZyTvaMko9a\n0c65tHeOFyYK+rVr3iQd+3oEQWDH7nqUMkhpE4P1LcjBFOt9hXGmyoZJzK22Z6+zuOvVoLXXx54T\nY5Ta9Dy4u2FeMdxNjYVSyOtBizydyjI2FCSvEsma1Zj9ATT5PCGzktKi2ndtXHaXEVECXf5igZV3\nQqNRsav6RgAGXCY6/vNfsVfdDYjcuqEdSSXQ0TrJ6SOjAHSosojkSc4UXPFGV4CclGO7exOisLhm\nY3VpocVoV0wkl3mtPFYhKrht691IigBxjZWKqbPImdv5rOqj3DplQpWV8FeFoekgv3nmb+l+5SBy\n/vovB7uy9L8PANlYnOhcElpZSfG8t6td5mJ8APyGctz5ERAE9p2e4OEPLd4s9e047W3nyNQJRiPj\nxLIXr3rsOhvLrfW4jaVoFGoy+TQTsWmGI2OcnG6lbizNDadiGJMSA446IloHojCLJBgwTOQZmR2h\nvX6WlE2LRa2kzKDFY9DgNmjx6DXolPOTdjzpC/ObYS+6nEzRWAy1QYWsyxDsLgNkZIsXy6yIdjSC\nUKzCfsO977hPm8PIyg1lnDwzSdu67dT0n8MxncGQyJO2TjI50EgdCo4dGmL3R5sWcmk/EPjDSX74\nTBcqpcgffLQZrXp+t4mWWhsatYJjnTN8bGfNu5KstVgM9fqRJJm4xwB5CXO8EPMNmhU4DNdWDe31\nWO0GBAHUyRxhBSRyefTz/M7dsW4tLz+7l6TNR6I1x/BL+3Ft3AlT+ymtHMQ7UEM8msZYYmBMq6Yh\n18uhgAlBH0atkcnlFGx1b1z8c9KqcWvyTKSdTE8fo6xi14XnGq0N7FvRBucgrypiVe9jdIU2U7nr\nM/zlCgMHW59kf76Pwdos30s8zcofPcFN1nV4ttyIxu1Z9LG+kUQuz68Gp2m2mlhnN1/148GS0X5H\nNOkEcW3hza+pmL8K0urqZvq1pwlIHqonT4JZ5uzg1dfB9icD/Ljjv5BkCZvWQn1xDWUmNwalgayU\nYSruZSQ6Rqvv7EXJZY60igdO5XEOR5CBWb2RfsdOxIxEt3uce872EFa0MEk9jrYAGZOKUH0RZy05\nzoZe8yBYNSo8eg2e88b8LQz5vmEfjw970StF1k6nGclKOMqLOfTyJBEEzM4gWVWGG/vTIINoNqC2\nzq85w/ptlfR3eQkntXSvWMuKcye5YSDNsy0K0hY/iZCTwS4foe0JLLa37//7QSWXl/j+XBz7sx9a\nTpnz7ePYb0SjUrCm3s7RjhkGpyLUut+dhK3FoL+74BqPlxlQhjLo04XPeNysQjOPENnVQqlSUGzT\nE55Ng1GJN5mhyqSb17aiIHB79Y08Pf0r9rc4WPbUHij9IhmVk/W1YxxJVxAcU+C1KlCQRx6OIKND\nUzpGNBtjnXMVJvX8Pw+Xw0qHg8nxIB3+AKWlcRSq13QAPrJ1B092dDFYvILSSA+rpvcz8ZtR9g3t\n4uZ7H+JWrcCPXniUHs5xulbgTK6NliePsDVqx7NhJ6aNm1AYDJc4+sLIShI/7ZtkNJai2nTt7iVL\nRvsdMGbjzJgtSIosVuf8++eWGUtJWwNoU5WkxSLMuTjBqEA8lcWgfXMy1WKxZ2Q/kiyxw7MZpaBk\nJDrGueEustJrJWcqUUWVuYJKcxlVBg/O1hHSz76AnJ5r+qBU8urmh9COJphCRjtbzcsbhnngxcPY\nEz2crN+OOmrBddqPpBBIF6vRVBUhOHQEcnnOhmIXGXKbRoXboMGj15LOS+ybCmJUKrjPbmHvnjOo\n1ApGB4NMiHmQRPQlIbLJPOX9YTAo0Ja8uczo7VCplWy7pY7Ay720r9lGQ3cbtf0JlI06LLV+Jk86\nqEPg8P5B7ri3edGu+/XCrw8MMDgZYXOTix0rLx3Hfis2Nro42jHD8U7v+9Zop5JZxoeCZHUKcnol\na1QaMtmC2zZrffcnejankbFQDDDgS83faAPsblzPC+N7SDtmOePwIPzgBzj/6IsEU3tYVXeMkRUf\n40BWZIXcz9nxwvtnL0kRzF6ZAto70WI18/x4kAHJwybfMYrdN194rsxaSlFlD9FhLQerP05Z8ByN\nwTaSh3/KMyN9lN29mt/Z/Ql+tmcNxwdPoCkboG2ZwNl8nKaex9nw1C8oXb660JhkRTOCeOXu/bws\n84uBaUZjKVZajWy/DC/slbJktC9BIp7ClIszpDIhGKIoLqObjSAIlNSYSE6Cz1BOleynXTZwqtvH\nztVXJ/s0lJrlyOQJREHk0MRRAERBpNTgospcTqWpnEpzOaUGFwpRQWpkmJkf/oTUyDAoCqthhc1G\n692/i/oVL1lkUwwc5QAAIABJREFUgiqBr390DT96+hiHVxnZ2RqgbnIvozdtxTJcSyiQQBdMQ6BQ\nJlJj0VG9thRtRRHeTJbJRIrxeJqzwRhngwVDXqxV8Tt1bo78phNZhmwmj8qhIeyTMFiShMUxdnQk\nESQQVCKG+obLug7VDXYa2qc4nhHpaNnEqtZX2dKd4NXmMeJiHQlJzWifn5A/jsW++DPw9yttfX5e\nOD5GiVXPQ7uXLci93VxtxaBVcrx7hk/cXIcovv9c5EO9fmQZ4m4DQiSDyWSCXKFGW+G4du043w67\n04hqvOCun0/Z1+sRBIHbqm7gmfGn2L/SQfO+SQL/+mOm7roRj72PU7kUCkGDpjOCX7IjmALM5oKU\nGlzUFVdfjdMBwKJR4dGrmUi48Pmexezahqh4Tcjqk/ftoPXYKKePjDJlXc20uZ41U/tZPfoyIz8Z\n5R9a7OhWyrgkE5P9q6irVZIwddLeIHCuTs+KgS42/NtJLGoz5s1bMdy5G1ljXtBnXJZlnhrx0jUb\np9as475qF+I1DAUtGe1LMD44gVJQgSBiLL78BIeWulqOHp7GbyinVOqhXRA43DF11Yz2i6P7kSj0\ny91YspYdns2UGd2o3+DOk1IpfE/+ktBLe0CWETQa5HQafXMLyU8+zNCLgxjzMqPIfHhbDdYKN/eE\nBL7foqd6Ik29N8rgmR7cn6hkZaCB44eGSMazKJQi0dkkZ14eRKkSWdZcwt3rPFga9ITSOSYSKULp\nHDfWl3Bm/wATc7Why1eW8LJ/AhBoWC4xmEywsj8JOgXybBZdXf1lXQdBENh+Wz09T5ylY9VmmjpP\nsLI7wfEGHSU1YSb67dQjsP+lfu755KrFuvzvawLhFD98prMQx777nePYM3Eve8cO0e7v5DONH7+Q\nUaxUiKxb5uDgmSn6xmdZVmG5FsNfVHo7Col0CZeOFr2O1s4Zbs1EiOlEbLbyd3l0hWQ0VeLyyr5e\nz211m9kz9iIZ+ww/X9PE/ecG8I6k2Wv+CGmFjrrUMN4JCVkUsVZNk5Qldni2XPUchRarmYlEhsGc\nA5f/JGbXtgvPKZUKNmyrprGllCP7B+nv9HK6/A4ssRGafEfYfWyK1pkWhlZ3oVmRYTSvwJZxs85Z\nxWB4hHP10FmnY8VIlvWHnif0/LOIOh1qTxkaj6fw2+1B4ylDYXpz6+XXs3cyyAlfhFK9ho9XOjnT\nF6CyxIi9aP4ejythyWhfgoEzfehUBbeH03H5b0ijrYE9lnbEQDmWcArUMDgZIS9JKBbBRfN6wuko\nr0wcQwD0KgP3N9yN7i08A7H2Nrw/e4RcMIDCZCKfSCCn01jv+ijaD93JI4f7KZpIkEQmZ1Rz67pC\nSMCyfhO3HHmCF7eY+fQzIW7qGeeR357hTz5Ty6caN9F2bIwzx8eQZdAb1EiyTEfrJB2tk3gqi2lZ\n56Gpzo4oCkx2eHnlpX4AVm4so7zJxSM/nkQ0hklIMVb1JVFmZRRWA5KUQe2Zf1jiPOZiHTsanDyZ\nS3Jm1TbWHd/Hus4E/esnme63E0dmajhEwBvDdhlx2+uRQhz7HPFUjodvX0b521wPWZYZCA/z0ugB\nzvm7kCmUMD458BwrrK+tzDc2ujh4ZopjXd73ndFOJbNMjs6SManIAzfVONm3pxdzJsm4S0V50dVb\nbc4Xm9OImJNR5eTLXmkDqEQlN5VvY8/4S3iNFn7+qS8jqNUocjk8s1OoWyP0FOkhKiMaQ6hRs7Fk\n7VU4k4tpthp5ftzPoFxJk/cYJsemN/V5MJq13PaRFTStdvPKi30EqOQVQxl1/tNs7T9KRbCRiV21\nnMqfI6gYI+gt1Hlr5lbt56qgo8rB6qiR5u44loEBUv19Fx1DYTaj8ZSh9njQuAu/1W4PCp2OE74w\nL08GMeZlrNNp/vdLx4gls+xc5eaz1yDJGJaM9iXxjU9hnSv3qn6LHtrvhF6lQ+cBApCVDKjkHNm8\nkoGJCA3lixsDeXn0AHm54A34UNUtbzLYudlZvD//GbFTJ0FUoCmvID02iqjXU/K7X8DQsoqf9k6i\n6ZtFAMaQuXdnNQn/IULRYUyrtlL9WIaONVr2bTBy+5EIuzsG+dtHX+VvHryDjTurWbG6lOOHhuk5\nOw0UVgSCIDAxMsvEyCxGs4bSsiL65jSqPZXFbLu5jn947CQAdY0Z/KledncnQKMkPxNB39i04BjU\nmo0VHHm6nc7mDbS0H2ZNb5K2ZWNoDcuZSWqpkeClF3r5xINX/4b0Xubxg4MMTEbYtMLFzlVv/pzn\npTxtvnO8PHqQkWjhJlhlruCWip20ec9yynuGjkA3zfZGAJZXWDAb1Jzs9vKpW+tRKt4/laUDcwlo\nCZeOUhR0jYawZCMIQMikpNpwaRnja4HeoEZvVKNKZJlVCmTyEup5XmNJlumejTOVqwFZicI+QmS0\ngQYklOOgzOYojfSyV16P2jVBPB9jm3vTWy4AFpvzSawTCReJbIZYsA2Tff1bvtZdUcx9v7OOzrYp\njh0Yot+xgbGiZTT6jtD06wPU3XAPvwpmSKpnWN4k4c+PE84UarllZFpNUVo3gHlrFWu0NWzJlqKd\nCpKZGCc9OUGiq5NEV+dFxxxvWsuzjTeSGo8y408zDDhEmUZtBnNiEFgy2u86UjxOTF2Q66usrFvQ\nPhoaPIy05wjoy6lQhhnI2zjWNb2oRjuWiXNw/DAAVq2F7a9TLJIlifCB/fgf/xVSMommsgopmyU9\nNoqmvJzSL/0RaoeTwzOzjAwFcQTShJEx2TTUqJ8jMl2ok86kZtCtr2HbwVH+8w4by6dkqoZ9NPWO\n8ZX/OMRffmYnZrOWm+9YTss6D0f2DTAxMosgQM0yO0qVgsEeH32dXgRRQBTg5juWM+GLca4/jGCI\nsNxtYeZACH1aRr9+BYmT7WjrFnbdARRKkTtXV/CDKR+n19zI5iMvsPFcgtF1M3ScLMeFgDwRwTsd\nxVlyaZfY9cqZfj/PHxvFZdG9KY6dyqU4MnWSfWOHCKRCCAissjdxS8UN1BRVIggCLr2DU94zvDCy\n74LRFkWBDcucvHx6nO6REM01734ceL50tBU+7wm7hodbyvjFi72UpgqKW8EiBXbde+NcbE4jE5EM\nmNX4Uhk8hksb1WQuzyl/hCPeWULpQlKqTb+CgNBOJjYMgTLsihjLxl6kdc1qCCooKvcR4+omoL2R\nFquRiUSaYSowzBzGaFuL8DZ14aIo0rzWQ12jk2MHh+hshTbPbhyxEWr3P85HXfU8aWqi+xUFf/6J\nj1Jky9Ib6qc72EfPbD+pXJpINsaBbDsHaEfv0FFXX80G1y3U6tyo/WEyE+NExiZ5KaDgiOxCbvNj\nAWrFPFpJCZIIKR2W7LUTFFoy2pdAnYkSM9chKxNo9QtLWGpxLWNIfZA4pZQwzAA22nr9PLjrnbed\nL3vHDpGVC1/EO6t3Xei+k54YZ+anPyE10I+o01F8622EjxxGjscxb92G89MPIWo0TMZTPDfixdEb\nRqawyv5YVRu5ZIDBkJupqIVtFV2wUcAe0bJFqOD5dUN83p9lR6iNoSk3X/3JUf7nJ9dTYtXjKDFx\n1ydXMToQ5Mi+AQZ7/KjUClauLyPojzPcF2DVlgqMZi0/e/IcIKAtG2bGr2ddVwJJIaAucpEAdLUL\nN9oA1dVWyoe99DStYXXbQZr7kww1dQEeonothkSOF57t5sHPbbii47wfCUZS/OC3nSgVhTi2TlP4\n3Mymw+wfe5VXJo+SzKVQiUp2eLZwc/l2nHrHRfvwGEtptjVyLtBF/+zQhWSljSsKRvtY58z7xmgn\nExkCMzHSZhW6nECxTkXXcIjPRrqRBBgp12JSvTdCKXanEeV4oYT0Ukbbl8xwxDvLaX+EjCSjFAQ2\nOMwsF1WcPFBBwN2OsmSY3rQW/bY4lfJn+N6JIQR1gpg4Q7W5gnLTtZNtbbaaeH48wLBiBcszT5EI\ndWCwXroFqFan4obdDTStLmX/C734JivxG8qoDJ3lgcBzvOjYzHd+fZa/+MxadpZtZWfZVux2Ix0j\ng3QF+zjlbWMsOkEil6Td30m7v7DCNkp2FP5GguNOTJJANRJFCAgIIImYdTFK8eIc68G95sZrcHUK\nLBntS2CQkoQVWgRjaMH7KDO6iTtmMU2UYkxLoIRQLINvNomj+MoTFxLZBPvmdMFLDS42lKxBymQI\n/vYpgi88B/k8xvUbUDkchJ5/DkQR54MPU7TzRvKSzNBUmJ+P+9BOJlAlcviQKbWFKCua5bEzy+iY\ncSDLkMitZ1dtG+rbnGwYCNLuMvPCRpk796b4SGAvP1bdw//705P8yX2rqCsrQhAEKutslNdY6Doz\nxfFDwxfUlvRGNWs2VzAViHOiy4ugD7O61o7y5CnMcQnl5g2kx8dAENDWXLlk5N2ry/nn3glOrrmF\n7Yd/y7K2EKaWaU52uTELInjjjI+FKSt/f5YoLYTz9djxVI6Hdi+jwmViIjbFy6MHOTnTRl7OY1IZ\nubN6Fzs8WzCq337SurvqJs4FunhhZC91xZ8HoNZThM2s4XSfj4dyeVTzFAB5N+meC+skXDp217s4\nMxDAnvTjSM8y7FYjFxe9ZwRj7C4jqp5Cwtwb49qSLNMfSXB4ZpbecEH9r0it5CZnEauLjHQcHePA\niUL+icdZxYRhmLQoc3piFMo1ZGftGGr6kbi2q2x4zUU+moCUQkNk5lX0luZ5XXe7y8S9D66lr9PL\nwT29DFtXM2WqY6f/FOPxcf7xF/C/Ht6I1Vzo0uYyOHEZnNxYXkh4GwyP8PLIQc75e5DyEsqQGnMg\ng0cyIlI4vtYYQ+9KU1tipDynQZ7WkMw5UdgclxraorIgoy1JEl/96lfp6elBrVbz13/911RWviY8\nsnfvXr773e+iVCq59957uf/++99xm/ca8WQGNYUbjdmx8BuOIAg4V9hITkAub0CpkcjlRc70+7l1\n/ZVnou4ff5WMVMgkvbv2wwiSzOi3vkF6eAilzYb9vk8QPXaE0HPPgrmYwO4HOJoqZuSnJxnzxtE3\nFGNw6vD0zSIhM4HEfVVhZpT3cvcuN192GPjJc9282jmDzXorazXPoqmVuRkHT5bEmFxdjrttjJty\nL/Ji+na+9fNWvnDXCtYvL8T+RFGkaY2H+hUuWo+O0tk2ya6PNKHWKPntnh5kQO0ZYLmyHEVHFEmA\nsjvvZeTrXykkfyzQw/F6SiwGKkUl/c0rWXlmL8uHEyRWdaBSOFHYLTAT54Xnuvn8F6683eD7hd8c\nGqR/IsyGRgeO8ijfaf13ukOFhByX3sktFTvY6FqLSvHOmgI1RVXUF9fQGehhLDpBucmDKAhsaHTx\n/LFRzg4GWdtw7W5qC6X1VKE9ZFanZF2lje/++gyrw4VrcqZeh9t4+XXrVwub04jqDY1D0nmJ03Mu\ncP+cLnmlUctWVzErLEbGB4M8/ZtTRCNpzMVadu5uQLLV8q2T30FZMkx2dDknk+OAA4VtCp1Kz1rn\nymt+bs1zLvJJ/UZqkodIRfrQFc2v7FMQBBqaXFTX2zh8YIjOU+N0lt5AcWKK2wf28qMfx/jSF297\n03axZJYTx2OMtHtwZT0UIVww1Chm0UmjlERHcY3NYmgreDXDr9u+U1HElvVvHX9fbBZktF966SUy\nmQyPPvoobW1tfPOb3+R73/seANlslm984xs89thj6HQ6HnjgAW666SZaW1vfdpv3IgO9EwgUDEZl\n6ZW591rKltEm9RFVO3DoM0xF1Zzo8l6x0U7mUrw0egCAGnMlTbblhPfvJT08hLBiFZMrthD9z5+j\ni4UY0ZfwhG0nydY4EEchCpTUFZN3G7D3+hDyMIHMqloFt9762YviSA/dvoyRmShPHYtQW9uIqaiN\nZXYvbrWO3zQk+MMJG+tGfQxtPMBI5Ea+98Q57r+5jl0byi/MkNUaJZtuqGHTDTU4HCbO9sxwtHMG\nlSGB3jZL7KyP2nCe6ZpyqlIp5Ezmil3jr+fDy0v5Xvc4ratv44ZXn8R+KsiNq8c4PamnVCFAMEl/\nv5+6OvuiHfO9ysmuGZ47Noylwk/A1ca/tBdWmA3FtdxSsZMVtmWXrS+9q/Im+mYH2TOyj883fwYo\naJE/f2yU410z73mjHY+lSUXSZIrUrPZYSWfzdPVN8fuxQXIGBSOlam56F+VL30iRRYdaAjEvMxlP\n88yoj5P+COm8hEIQWGszscVVjMegJRFLs/epTvq7fIiiwJrNFazbVolKpQCs1BfX0Mcg2bFl5MYb\nUFinyZJiZ+nOeU3aFpsWi4kXxgMMylXUcIjwzCtozfWX5eVQqZXccFs9q9Z5+O2v25mllLDORcNU\nL09/6yf83t/9EQBneybZ/1I7+VkJtajFM/e5N2RmcUUHccaGMWRfa0YS0YuMlGiIq4rJKEqIaoz4\ni6BybQnXyiexIKN96tQpduzYAcDq1as5d+7checGBgaoqKigqKjgaly3bh0nT56kra3tbbd5L9Lb\n1o9yrtyrruby6oTfSKO1nlPaQ8gZO+5cminUDEyGSWVy89Z1fisOjh0mnS/Msu+pvxMpkcD7m8fJ\niCr2eTXc1PUD1HKOY9Zmhhp3sLG0iMoSE1UlJnQmDd/rHkUZT6ObSJFBwC8K/Lddm9+U+KFVK/nS\nPS381X+c4KfDJn5vaArt3VXcbIGfZQRe3mbhlqdC3H5ugl/cfJTM2A4e3duPfzbFA7fWv6XAxjOH\nh5FlEEp7uN3ixvhCKwDO2+8jOVBY3VxuffalKDfpcKtVDDU10djxEpXjcfRr+zidd+GqryLc7eeF\nZ3uY3njlCSXZdJqBgVayFiuCRoWsoNCaRwSEuUIpqfC70PhNPv+v0Alu7jEXXiejlo1oZTOCKBa6\n/AiFH0GYWxEIAogX/gyiULjJSTLIMnJeBkkmE4sQn+hnlTZD3i8jBCysVHoo0pjQiCr62sfpY3xe\n5ynJElk5xoc+fjuN1gbKjW5avWfxJnw49Q4qXEZcFh1t/X7Smfzb9uJ+L3DowAACkLRouKPZTftA\ngIbwEGopR1uNAVkUKNG/+5nj5xHFQm/tyViWkELg1ZlZTCoFO0qsbHAUYVIpkWWZzrZJjuwbJJPO\n4XKbueH2hjeVON5SsZO+2cHCanuoBV1DB3lgu3vxWnBeDlatCrdew1A8jVDUSCbWRTo2gtZUddn7\nKrbq+czvbaazY5qDT59jong5qnyKn375H4mrzMQ0LpSiFqUC9JlZXLFhnNEhZDWE7E7GHA3o5CLU\nJW7SZTrGDGP0pLuJ5+PAFG5DCc32Rm4u277o1+HtWJDFiMViGI2vvfEKhYJcLodSqSQWixUUhOYw\nGAzEYrFLbvNeJDg9g15tAfI4nVe2+tKr9MieHAyBLlXQ+5Zk6BoOsWaBK5B0PsOe0X0ANNmWU22u\noPW7P8CYiDOtdbLbfxxJpUH58c/ywA1bLyq7yeYlvneum4ykYsXgCFHJwBgSt26owFb01gktHruB\nh3cv599/28m0rpSSXw1S9z/upCl9mnNE2LijGdOBdnacHaHjZhum7tW8fHqcYDTFFz7ShEb12g17\nyh/nSMcMJrOEbJnBPaZG7csyVGJk15qVTP9bwQOjXcSVNsDNFXZ+1j9FW/M2dr2yB8WxIB+6YYDu\neDlZlYgqkeXs/qFFOpoLvK8zvnNIYp68Iock5pEUOfKKLJLi/OMc0txPXnzdY0UeQQYhrUOIFyEk\nTYgpA2JehQIu/Iive/za3948YRLx8Hq5HQlYeNaGhl99/2eU3n8X28tu4efdj/DiyAE+3XgfgiCw\nsdHF04eHaev3s2nFe2el+kb6B4IoAKvNgFIhcvTEOdaEe5EFOFmrxW0oYUPJmnd7mBdhdxoxjs9i\ncpWwyW2h2WJCOTdBDvnj7H++l+nxMCq1gh276lmx2v2WE+gm23Jceide+xT5QCl5XYBGawNO/bvn\ndWqxGpkcTzOpX09prIvIzKvzNtqyLCPl4mTTAXLpINlUAIcuwJ13Bnn1aDEhr4Mpc6HSQZONUpye\nwUgaVbEdzbJ1nKv4MN1KgXKdhs83elArLp5s5qU8ncEejk6d5Ky/i8mRaVK5FJ9Yds9iX4a3ZEEW\n02g0Eo+/tiKRJOmC8X3jc/F4HJPJdMltLoXFoke5yEksDsc8ynsyQeLqJgRFDJfryhOUlm9bxWBv\nhIzSiFYLqTR0j4fZtW1hiVZPdb9EKl/QCn9wzb08/vgJVrQdIanQUJHyoivz0Pi//m90noszPzOp\nWX5y9FWmM6XURkaJThtIABmdkofuasaoe3t32EduMjHqj9P6Qjkfkicwey18ovF2/urMb/mFe5ov\nV3tYNjTBYE8H63Y30nXMQWufj3/81Rn+n89tpthUEDj450dbkWQZddkgaww60oemUQPTq9bhdJoZ\nGRpEVWTG3VS7qIk/O+xGnuobZXLZWkb6D1E5nYTEJKfDfXz8U7s5dmTk/NJ3XmSzGRKJMEJ8lqJI\nAH0khkKSkUSI6DUY/w979x0e1XUmfvx77/Q+o9Go9y4kgZCophkMtnFvOLZjO/HacRLHSTZOnN3N\nluxmN8XJZjfJbpxkf05ix3Hce8U2YMAY00WXUO9lpBmNptf7+2MwhtiAAAmBuZ/n4QHmzr33zGg0\n7z3nnvO+gQRxUcWYXs+I2UpCVCMmRIS4AmVchRAREKWJXcMsCSApBCRRQBIEYiIoogHs3kH0YT8C\nMUaNViSlgsN9++McKdlnTwhxEkKMhBAhIUQ5cg0ggZBQIEWLkOIFbNjzJxTpV5BiupLtzgZur4+S\nakjh8gWFyaDdOsJVSyb2ImyidHSMIAZihM0qHri6GrNWyXBbP+kRNy05aiJGDd9d8hWyTBM7C35c\n30MnUFCcyoGGfq4yWqitSBY1ikXjvL+mhffXNpOIS1TUZHD59dWYT5Kt67ppK/jd9icwVOwhBlw1\nbdkZt+9MLNark0PkUT2ltiJ87lYMGg9688eJluKxEKHAMGG/k1DASdg/TCjgJBQYJhELfeKYolLL\nJUu1OMd8bNmiILcwg8VLFpKaZkJ9eOXEm60DNDb1kWXU8u35ZRhUnx6jMtLnsqxyLmNhHzt691Du\nKMZxkkxqE+W0gnZdXR3r1q3jiiuuoKGhgbKyjycJFBcX09nZyejoKHq9nu3bt3P33XcjCMJx9zkR\nt3tiax87HCacTu9Jn6dJhAmoFCh04XE9/2SKLGUMxF8hpKrEqhcZCMf4YG8fn1tafMp5a6PxKM/v\nex2AalsN//lIE7N3v4qCBDriiDod6V/7Jj61Cd9RbQ+4D9DQuZVt0QVYCBDdkTxvJwmuml9E0Bci\n6Pvkh/1oNyws4GdN04g7t9Dy5lqqFv6Q5Zm9vNm/iw9mxqjvUbJsu48n0l/lnqX3Y9Ao2LRvgG/9\n93t86+YZqBQia7d3Y7eqiBpamDOmIdETpidNRcWMZfQ1dRAZHsZQO5Ph4YmvP74wO5U3+jw0VFSR\nP7Cd0GYXK65pobG7jlU3nLiAiCQl8I120tOzBynQgm5omHiLh3hHAAHw6UT2lJkYTS1mVfF0fI8/\nChoRoTMKIsRq7KguuZaSqoVHbkEkEgmikTiRcDz5dyR2+P+xYx4TBAGVWsQv+XFGhugN99Ed6CEk\n+A/30OOkm+yUpZRQbiumyJRL1673UG54F6E5+RlwFZtIWfU3LJi/+MhnWkrEiUWSvZFw0EnPWCct\n3j7agx66o1GOnpecphDJlQTywyJFxmxSCmp458k+nLE0Kg/oaDC+jEa/Ep3uCv5tYyOX5eYx024i\nx2Fg+8FBOrpdk1os53T9+YU9yWsRi4ZEKMbbG9cwzZ3M2Le3VM+t5TeiCulxhs78e+Aj4/0eOhGN\nPvn13dE6THahld5ON+tXH8LjCmIwaVi0opTCslTCkdhJz1VpmIZJZcQb9WHVWMhV5k/I997pEoAs\nvYYDw2NcXjwX3G207nkOlS6NWHiEaMhFIvYp3w+CApUmBY2hAJUmBaXWjlJjR6WxIyr1CIJAKrD4\n0o/ff89Ysmb4zuExXmgfxKJSckdxBoHRIOOJPtWmGggx4Z+P4zmtoL1ixQo2bdrELbfcgiRJ/OhH\nP+LVV18lEAjwuc99jr//+7/n7rvvRpIkbrzxRtLT0z91n3OZJi4SAAyOifmSyTFmkdA5QaokLRxm\nAAX+YIyuQS8FGadWh/X93g8JxkMICOzblEbaUDNl/m4EtRopEiH9C19G7fj4/lsiHsbd8xbOkSbW\nxFciIpFxIEogIeJGQmvRsrRufKlCVUoF99w8i4aDqykY7KW7sZ3Lylex1dXKB4xRNd+CdsMIyz/w\n8LjtSf7+sr8l1arj5ffb+dHjO8hLNxFPSOSWenHo1MQ3uBCAbUXpPFCUS2hXMjuarnji7mcfbU5G\nKu90DuLOn0VT/l7KO8Oo+ly06j9Akgo/0bNPxMOMuQ4x0LcPZbQDlRRGc8hHbPcYUVcypA3alewq\n19NdUM7sfWOsXH4d6Y48nJ19uFe/ibs4FeOgG9XuEaTmxzg4+x0Mi64jL78OURTRaEU0pxTMkpmX\n4ok4Pb4+mtwtHHK30jLaTn/PJt7vfJ/apgBz9wUQYhJjKSoGl8/lsuVfRHlUWkhJkhgIDtPkbqHZ\n3Uqzuw1/7OOvqVRJS/ZwiNxAhFylAnOWGTFFQ4IgMIxnaC0zFoisW2clJpWj96wlLLyCpJpBUFXN\ny51DrO4ZJq06lf4Pe9h5yMmi6Wdvze94xOMJQp4wKuDiufnEYwF27Wtnoa+dUaNIvDiPuZn1U93M\nT2V3JCfK9vd4WPdGI42HJxTW1GczZ3Hhkd7jeKgUKpbkLOC19tUszJqLQpz6+QfVNiN9gTBtsTSy\ndZlEAr1EAskZ/gq1Fa2pGKXWngzOhwOzQm0+bjKWEznk8fNCxyA6hcgXy7OwqM+9i8uPnFbQFkWR\nH/zgB8c8VlxcfOTfy5YtY9myZSfd51zl8YcQpeRwUnFp8UmePT6CIKDNM6BsCyMEP/pQSexuGTml\noB1LxHjhAVeCAAAgAElEQVS9/R0AIoM5iEEVN0X2Jo8WiWBZugzTrI8ThYR93Qx3vkgsPMo6LiOI\nlrkqDX39fUcSqXxxSTEq5fg/6Gk2PemLFsCbz/D+U29y/ffu5aay6/nd3sd4u1DHdZ068jqD5Ozu\n4Xnbq9y+cBV2s5bH3mrkYKebLIeBUc0HrAxDotXPkE1JOKMalVLEfWQS2uQMpaoVIoXREQ7ps9lZ\nnkNpVyuhD0eZ/rk2Wjo7KS0oIBbx4Bk+yMjQftSJPkRBQuePEWjwEz3gQRGJkUCgNcfAzmlqBh02\n0sPTuGzjFqruu490Rx4A9muvx7d7F7a2QSJ33cS+He8ybd8oyvXdRJr+jwPzCrDPvJL0rJrTug2g\nEBXkm5OV21bkLqatbRMDO97CtGUI7ViMoEZgfZ2JA0VaBEULjbt+R5mthBxPGg09B2lyt+CNHFUL\nXWtjmi6PrPZRHJsPYvBGETRazBctwLbsEtSZyYCbiEcO93aGiQT6yLb00uUtpKqhkM2L21GEt6KO\nNlCUejXuaCp9YpzU+Zm86/aQ7rFQYtaf1apIJ/LqxlZUvihRo4raEgfDnW8gDnShSkjsK9bzhapb\nprqJx6VSK7Gk6HAOeHEOeLGnGVhyeTnpWafWCfjIivwl2HU2Zk7BMq9PU5Ni5O3eEfa5fcwovoVI\noA+VOgWlxvaJnORnoscX4i8t/YgI3FmaRbpOc/KdptC5OQtsiu3d00NCSA5PVJ9iWcgTyaudDfua\nGTAVY9MlGA3CrmYn1y4cfxGCdzs2EYyHkBICVm8NX6kME3speYWtyc3DcXPyS0aS4ngGNjI2sBGQ\naDRcTveYjTKznuE3k5OtBpHIzDAxu/LUZ8VWr1xC8+rnyR1q5rG3mvjSVZVUppRx0HWI/qWFpD/V\nyOIGP09nbKbBXsHC6TXYzBqeXtPCZUvtDAyGELd6kCTYPk3PvNxaAIItLaBQoMkvOOU2jdeSyhJa\n2nwEUyvYX9xLTUsIoXmMkPQyLW4FalwAaIGhDoHE3gCWniEUUoKoqKaxMIMPp0fxGRSoVVXUtyko\nPLSWom9+myzHxz9LUa0m46576P7JDzG8toEV//h9XtvzAvZ3tlDQFUb1UhP+Q30cqC8kvWIl9rSK\nUw7ekpSgr3sb3qZ3UX7Yg6MziCTA3nI9ystXsDizkmxPO4fcrbR7umjzdEJHcl+z2sSs9FrKzEXk\n9AUR1n1AqGUDAKr0dKxXLse8YCEK3bH3Q0WFGrU+E7U+E0NKDRffGeSJ/3mfoLKAqvYAjYWDBBNh\njJ5XWFVyFd1CKc8fHCKqV/LooT7sGhXz0izUp5rRTnHSleYuN0YgM89CJDjEppa9VDs7iQvgm1lG\n9lnMBnY68ovsHBjrY9aiAqbPykFxBnnelaLyrBQGGS+7Vk2mXkPLWICokIHeUj7h5xgJRXisuY9o\nQuLzJZnkn0Jt8qkiB+1P0dLUgqSyokiEMJomLlF+RXE9rug6oBizSo07GKFr0MeoL4zVePKru+Ze\nN6+2rgYFpIQr+ftVdQz86/eSG9UaMr9yH6JKTTQ0wkjnS0QCvShUFoLp17CxM4ZJpaBoIMS+UJQE\nAn1IPLC05LR6PQq9AWN1DeKeBl5qOMT6XCs3lV3ND7f+N++Kce5eWUPoxd2s2uLjOdtzFFjyqCpI\n4Qd3z2F1x0vUxgRijT48RgWH7A7uKSsmEQ4T7u5Cm1+AqFafvBGnqTAth4xNr9CTXcHWqi1UtoWJ\nb/FgLjMSTyhodlqJdkBm5wBmZzIXtV9loSU3jx2zxvAqg4iiHbNqAUs2bEHrbSHvm98hL+2TozK6\n4hJsl16Ge/VbBF99g1tv+Rt2l8zhjXVPMH+rE9sBL6qWfYzN6WWospDsksswp5ScNHhLUgJnfwOu\n9jWoGnoQd3tIJKA3Q82eBXlcu+BOCszJHn91anI4PRgL0TraTlwdIV2RgT2mZWzjejzrnybsTs4f\n11dPx3bJcvRV1eMu1GIw6cjLVNM+qMLWYmRZaJgNlQk+DEdQtL7GAp2WW4y5vNxkx1JWyVAkxuvd\nw7zTO8JMu5l56ZYp6d3sbx9B5YsiAVdcXEJf11M0DjqpHIvTkm5gcdmykx5jqs1fVsz8ZcXnZd3y\n8aixGXk7EObgqJ+61NMbQTgebzTGHw/14Y/FuTbfwTTbuZGi9mTkoP0pvN4elKoS1JJzQo9rUBtI\nGDwIUhxbWKITEJDY0zryqdWVPiJJEut29fL07ndQ5scQUfKPl92K98UXSfiTw5sZX7gLRYoRV/eb\n+EZ2gJRAb6tBn3kZf2kcRJLg6owU3n+7AQGBXhJUFdupzD/90onmOXMI7GlgRqiLJ9+18r3Mei7O\nWcDa7o0cqLqMynYP4YYObjowykuWP3Nn7VeTOzr3wp4xhITEjml6rFIBRp2KQFMjxOMTmlTlePJF\nF31CJVFDLrvLA9QfDNC6zojemk5u0w7wJGt9D+tz6M2soHO+n/ZEKwIiGnUd6dFClr74DD6Nl6xv\nfoui9OOPyNivvQHf7gZG17yDsa6eGWXVFF/3TzxT8QLRTVtYsNcP74+g2j+Ga1EP/TmF5BStwGj9\n5MoCSZJwO/fg7FyLuqUPcbOLeCCO1yCyoc5E4UWXcl/RpZ+aFEOn1FKdWonOM0T7Uy/TsW0LUiyG\nqNVivWQF1qWXoM7IOK33c/HN8+j85Ub8hlwG+1WsatnE0ytT2BSK4BfUrNB0cEdlJ6HYPvRpdTRR\nxvZRiS1OD1ucHopMOuanW6mwGlCcpaHzNxu6MYxFEcxqhEQnzw02UdmanEzUPMPKZanTzko7zsRn\nNVh/pPqjIXKXb0KDdigW57FDfbjCUZZlpTA3bWKrLk4mOWh/CkUkuZRKVIUn/Nia/BxsbQO4hGzU\nJBCVMXa3DB83aIcjcf60upHN+wfQ1SVntF5ZeAlK9xijb78NgGnxfOI5Pvr2/wpJiqFU27BmXYLO\nWslTbQO4IzEuzrTRubGLREIiKgg4Jbj/4jO7X2+snYmgUjE73sd7sWoefnEf371jCVsHdrK6cx2z\nP38/0Y7/gF0eLsrvYYvpFRzmAsqiUSL7vIR0Kg4WallkTBYECLUmX9+ZVPYar/IZtbQ3dNCdVsn2\nad3UHAqTc2gfsI+4oKTPUoEzoxpxvpptiU0EYkFUijQ0mkWUOT3Me/0R+h0Cmfd9g7L0yhOe6+hh\n8sFH/0D+93+AUWPgb6bfwc6MGTxT8hwzdgxR3RIi+soAyqIxhhf00GfOJ7fkUnSmPCRJYsx1gIGO\nNWicA4gbR4gOhokpBLbVGOiuz+W2mlspshw/NbCUSDD05z/h2fAeAKqMDKzLlmO5aAGi9syGBfUG\nDUUFRlq6QuREA3QrS7n9jRYeu8JGQ9BHRFNI4YieQmMfCdeHlPIh5WoHfaZ6dofTaPMGafMGyTdq\n+WJZNppJLufp8oaJRpOlbGfVZ/HaoSfo90W5ojuCW62nfPbSc2Iy1oUuVasmQ6emeSxAKBY/o9sp\ncUnCF43hicTY0D5AXyDMrFQzl2SlTGCLJ58ctD+FKioQAzTWib+Kza+dS3zvRlz6bBxaBX0h2N/u\nIvopRRUGXAF+/eJeep1+0kuHGFNG0So0rMi/mN6f/AikBIppdmIzRvEOfYBCZcKcsRijvRZBULDd\n6WGvy0eeUUtVQsErrS4EBDqlBAtnZJLtOLPhIFGrw1AzHd/OHdxwkZ7nDwZ5cnUH18y7nL80Pc+r\nfeu4+d6v0/OzH8MaJ+n2Xfi9BzDs9yJEE+yoMhCNGFlcnuylBg8Xoz8bPe3ivBqyX/oZ/VnXEdDq\n2DQjzqx9MGAqZjSrmoKL0nAqNtE42oxCUKHVzEevnsa8xu2UrH+HjiwN6V/5KlWZJ14m9hFdcQm2\nFZfhfvsthl98nrRbbgOgLm06pUuKeCrjBZ5s2sXFO/xktQUIdwRR1nkZivYQ1eYjxQOoA0MoPnQR\nPuhFAA7laXh/ponZFUv4btHlqE+QclKSJJzPPo1nw3sYCguwXnsj+mmnX6v80yy8ro62X27EYyrE\nHRmm1NPOratdPHtlOgdG2+lXpfPCphnctdhCeWo/AU8TGZG3yAB8pgq2xGto9oV4vLmPL5RloZrA\ntv21Zz9sQzccQgLipr1sGPAwuyWCMiGxt8DKzdkXTh76c111iol3e0c4OOpn5nF629FEAm8kjica\nwxOJMhZJBuexw0F6LBLDG40fk5WgwmLg2oK0c6YIzHjJQftTqOJaYgrILM6e8GPnVtYzGvgLMA8z\nIr0kSCSiNHWNHlPCcEfTEL9//SChSJxlddns0rwHcbiq6DJ827YQamtFcKhRXmxGEBVYMi/FmFqP\nKCa/uIeCEV7tcqJViKwqSOetx3YC4BchIIhcu/DMq2cBmGbPxbdzB3PivRzIL2ZX8zAl2UXkmrLZ\nNriLxfXzsV12Be633iC6aQTzolSCuz2g1bC3VIPOl0N6ih5Jkgi2tqBMTUVpPf0h+/FSiAr0lhiW\nUTchbSF7KhrBtpgFFbVobG081/EEkUQUkyYPlBeRqrWyaONr2Bp20pqjwX7PPdRm1Z7SOe3XfTxM\nbqqfhe7wJEeT2sg91XewI206zzheJLvVzcW7g+i2jxI+4Ee3MEDCHyewZRRFLI7HpuHdOh3hgkzu\nqbz5SDnME3G9/iqj76xGnZlF1Q++z2h44r+odHo1JSVWDrV6KfU08+HMa1m24xmuWONkz/UzaR3r\nQFXp5e2mJSyYfROJWIjA6AH8rj3gb+RiqYm4Yhlt3jSeaOnn9pJMlJMQuKOxOP2xKOljUUxpap4d\nfB9RkqhqjBIVFCiXVGBWX5j11c9FNTYj7/aOsG14jAQcDsJRxo4E6RiBWPy4+ysEAbNaQb5Ri1mt\nxKJWUpBqpkSjOmu3YiaSHLT/Sk/fEEhGkBJUz5j4pQ9KjZaIGYzhESQhBRGw6MLsbhmhushOPJHg\n+fVtvLWlC7VK5EtXTyNgOsTmlhB6pY5aUWToxUcBUC1Kx5q1DJNjLqLi44lb0USCp1r7iSYkVhWn\nM9DoZGw02avoSCS49KICbKaJmfhjmD4DQa3Gt30bX/re1fzbo9t4fn07t1+/jG7v4zx76GW+c81X\nCOzfS/hgN65AHEMoQXNtOhFViHpbVbLNgwMk/H4M1SeunTuR7PVzqXpnK66L6ojEGolU9PNmvIOO\nti60Sh027SLiYhHTzDpmvfQYipYWDuVpsH3xC8zJOfX628lh8rvpfuhHDPzx9+R//weImuTPQRAE\nZqXXUmot5knb8/wxex9zD4aZedBP9O0hACStinW1evaWaFmct5Bri1eiUZx8wt7oujWMvPQCSrud\n7G99B5XZDJOUOGP+FTW0/Op9hs2lhLp7GKieR96+D4lsaKXk2hWs7lyL07GGht4carNLMabWYUyt\nIxZ2Mzb4AZcMryMmLuOQx8FTrQPcWpyJYoLv276+qxuNP1mpaciyn6AkcXmLAkskyH5bFosrlkzo\n+WRnxqFLDpF3eIN0eIPHbFOLAma1kky9GotKiVmtPBKYP/q/Qan4RG96IpLbTBU5aP+VnQf2ERGt\naGNeUm0Tm7bwI8r8Qhwd3fg0dqzC4VKdrcNc5cvnty/vp6l7lPQUPV+7vprsVAPf2fBrAK426Bjd\n8CLScARFrpW8S7+HqDz2XmQ4nuCVziEGghHmOMyU6rX8aW0DAG4BFDoVK+fmTdhrETUajDNq8W7b\ninakn69cU8XPnmzglbc91C6aQcPIbrYM76b+ni/T+e//iqEziKBWsb4wQiKsY2F1chlHsCV5P/ts\nDI1/pLJiPpHHn0MfXUJINB0pTZlrnoYnXgcKHVekmcl67NfEO7tozNdgvPM2FuRddNrn1JWUfjxM\n/tILpH3u1mO2WzQmvlzzBbal7eIZ3cvsLVRxaaOIXy2xtkLAYLHzjcqbKbONbz7C2JbNDP3lzyjM\nZnIe+C6qlMm9f6c3qCmtsNPU5KbMM8Y6/TyudByg5MAIsTIXI45lbPOt5Q9Nf+TL2jupsidntis1\nNmy5VyCqDFzav443xWUcGIVn2we4uShjwtZ1S5JEw4gX61AQCYkOYwfT1DpSG5LL/NqnZXCdeeJ+\nP2QT48bCdJo8fsyqj4OyWaVEqxDPu+HtMzW5sz3OQ51D3cRFNSrJO2kfhrza2aT6uwCwqxR4QloC\ngTH++fdbaeoepb7Mwb98YRbZqQbWND1DKB4mX6kgPxYhunEYBMi59++OCdhxSWLLkIef7+lg14iX\nDJ2aK/McfLi+jVg0jiRAl5Tg2oWF6E4hU9J4GGcn7/95t22lPM/GDUuKGPVFGGkqQC2qebn1TeJp\ndhw33ASAeNFM/LoECm8mJdnJWZsfVfbSTmBlr5MxqY2M5tuoOLALtboOmzaDUvvVjEkLsGpM3J1n\nJ/uP/0O8s4sDhVrUn7+JZfln3guzX3cDqvQMRt99m2DzoU9sFwSBORl1/NPcB8gpqObZ2SJvzFAw\nu2gB35vzwLgDtm9PAwN/eARRqyXnW99BnX52CnbMW1GBQIJBSwVprbtoW3oHAY2A+Mo7XKfPId5a\nRzyR4Ld7HmVz37Yj+wmCgDXzYuxZS7hcWEeG4GKPy8eLHUMkTiEn/InsbB8hrlWgGYviswxj1cS4\noteGLepjSG2ldvGcCy4InA+yDVqWZdmZ5bBQZjGQrtOg+5Qe9IVADtp/JeZLpqYUxeBJnnn6rOVV\nmMIuFJIfU3KUjgyzj0Aoxs1LS/jqdVUQaqP/4O/4cDBZsvJiSwHhJ7ohImFZdDGazEwg2XM4OOrj\nV/u6eLlziHAiwbKsFL5cmYvXFeTArn4A+iWJFJvuhEvLTpehpgZRq8W7fSuSJHH53DxmFNs51BYm\nT6jFF/XzZse7WFdcRs63v8vG6uQ96zJTxZElK6GWFgSNFk32+NKpThRt7Qwq9m9HqygmobqaoUgG\npWY9X8mzIf36P4l197CvWIvwuWu4vGjFhJxTVKvJ+OLdAAw8+nsS4U9fpWDVWPjq9Lv4Us2dfKvu\nq9xSfj1a5fhuawQONdH/m18jKBRkf+MBNLlnr/eoN2oor0ojpDJSHPPzQYObXZfWJSfDPfIws3TZ\nhBtnoxE1/LnxWd5sX5MsSXqYJWMhjpxlrBTX4hBG2TE8xmtdzmOec7pWN/WjHUquSfel9HOzowTf\ne42ISOxJz2VhwbmTXEQm+zRy0P4rysMpRkXN8Sc2nPE5UuyEdWrs/m4UCTABOrXId2+bycXT4gy1\nPIqz7Sn2efvojydIURlJf6kXAlFEne5I1rMef4hHmnp5vLmf4VCE2Q4z364pYHm2HbUosP6tZC8u\nIQr0I3HTxcXHlOicKKJKjaF2JrHhYULtbYiCwN1XTcNu1rLvQwtmpZX3ejYxGBhCU15Og/sgUkTD\ngqLkOti4z0ekvw9dURGC4uwusympvogEQYoPJYtGLM9O4fNpekb++yFivX3sLtURv+FyrileOaHn\n1ZWWYlt+KdHBQUZeeuG4zxMEgVpH9bgmm30k1NlB3//8AimRIOu++9GVnr3Ri4/MubgUAYleaxW1\nQ3sZic5h/SwT+AMsPPAGijEjM7gWu9bGa+2rebLpBeKJj3/nzGnzyMhbwZXiGlIEDx8OeXirZ/iM\nAnefK4DPLKAbdCMJCeZmj5IxlAGxGBFBSXRmGepxzBGQyaaSHLSPkoiHUEWSGdCs6ZOXzk4QBBR5\nBWSNJYfIU0SBbrcOc+BFhloeJ+LvQW0u561gsht+bY+dcHfyuY7P3YZHUPBUaz8PH+im3RukwmLg\nG9V5XF+Qjll9uPJP8zADPR4AOhNxirIt1J1m7e7xMM35eIgcwKhT8dXrqhEFJb7WUhJSgueaX6Vl\ntJ2oFCIxmk51YXLOQLCtFZj4+tnjkWfJpTvPyPwNb/JNQ4xFOpGe//wJsf5+dpXriFy9lBvLrpmU\nYbjkMHk67nffJtjcPCHHjAz00/uLn5MIhci8+14M1VOTR9pg0lBenU5IZSI3EaKr0clQ0Sz2lOhQ\nOPu52vkB+w+GeKDuPnKMWWzq28L/2/cnIvGP64qZUmeRlb+Sq8S1WPGycWCUNX2u027Ts7s6iPq2\no/MbEa0u5ufPZuyt99BIMQ6YCrli7qyJeOky2aSSg/ZRvJ5uxLgBRSJKZsXxk1RMhIyaGdgCA0hC\nFKsgMBbS0Od0ojUVk15+D5slE+FEjIywFvOa5H0/VU4um3LL+K+9nexx+cjWa7i7PJs7y45Nch+L\nxdnwdjIIRJUCw8DNS0+eGvNMGKZVI+oN+LZvRUokACjKMnPLJaX4B1PQhNI56DrE000vA5CnKUWj\nTvaqP0qqMllFQk5EFESoKkOUJMbWvEb3T39EbHCQ7ZV6AisXcmvlTcnnTMa5NRoyvngPAAOPPkIi\nEjnJHicWdY3Q818/I+71knb7F45cSE2V2YuLEJDottawwLUbT3MB780048m2UubtpKRtK6OjAn9b\n9xUqbKXsHT7Iv27+KS80v0aXtwdJkjDaZ5BbeCVXKdZixsfaPhfr+089cPuDUbqFQ+idyVsR1Tke\n1IFswiPJofLGrAJK0zMn9PXLZJNBDtpH2dPWSFwyY4i4ySs9cZarM6UvLkMkgYZ+1PFkcYrXWxaw\nzbmA5iEl73W/D5LEVWucSIeD7epZS3l/aAyTSsHNRel8dVouxWb9MceVJIkdmzoJHL433xqLU1/m\noCTHMqmvR1AqMdbVEXO7jwRhgGV12cyuSMdzqBQQGAwOIsVUzCv4+P0NfpQJrWhiKqqdqtzaBYRV\nAond+4k5nWyp1uNbPoc7p90yaQH7I7rSUqwfDZO/+PxpHyc2NkbPf/2MmMtF6o2rsC65eOIaeZqM\nZi3lNRkE1WbSEwmEYRe60Rk8M1dFzGxhsauBptUb0Sm1fHXGXVySu5hIIsqa7g08tO1X/PuWn/Nm\n+xqC2gwKiq7iKuU6DARY3TPCB4Ojp9SWJ3ftIsgWLK5MBCFB5YxqPG+vRSRBv8ZOzsyJKwwkk00m\nOWgfpbnXCYKILurBaj31ylenQpOfT0IUcPi6AcjSqmgfCPP8+jZ+tf4VYlIMdW8W+9UlCJJEe345\nQ1n5rMxJ5Vs1+dTazccsg4nHEjTu6eeZP2xn5+bkUHpAKRIQBW48w3Sl42U6PIt8bOuWI48JgsAX\nV1aQpk8jNpCcDBV3pzGzJDmTWYrHCbW1os7KRqE3nJV2/rXKtAracpIjFZtrDHguruOumtvPWhrL\n1KOHyVtOfZg8HgjQ+4ufEx0YwHb5FaSsvHISWnl6Zi0sRECiM2U6F7sbGO3KwIeeg9dOJyooyd70\nEsHuLpSikhtKr+LHC/+Ze2u+wMy06bhCLl5rX833Nz/Ewy1rGDAWsUJci54gr3U52eb0jKsN3rCP\n3cG3UAe16AJm0tL8mI0l+PfsRgB2Wcq4vHZyL9Jlsokir9M+itsLekAkMOlLCUS1GjEji4KhTnoM\nF5GmV3H/PXPY3zXIX/regZgSf38Z5a7XiSHyFtNQbR2kMTtEyDFGhkmDVhDwuIN0tboYHvQSjx81\nSUeA1liMJXXZZKToj9+QCaSvqERhNOHbsY20Wz9/JEWmTqPka9dV8+9/9hKVRHIV044kdwn3dCNF\nIujO4lKvv6ZT6uheWs3eklYMpWV8reZOVBNYr/dkPhom7/7pUUlXxlnlLBGJ0Pe/vyTc1Yl50WJS\nb1w1ya09NSaLlvLpmTTuESiURBz+YTw9M3ivZBvS7OVM3/oW3b/8JcXf/1cUJhMqUckMRxUzHFUE\nYyEanPvYPrCLJncL7WOdiAhkK59HUtbzQnsElShQaz9+IQlJkvjV9idICD7SPPMAKKvOw7NhI5Ik\nERFV9GYVkGU/Pyo8yWRyT/uwRDwC/uQXtag4s3uL42WtrEQbixDTDhNzhVBKsD/0IZqQjpTwPC7S\nBBg2l9NQdi0lJjvZoxEi+4doea+d919t5N1XDrJtYweDfWPE4xKCkPySzMyz0qcSkNQKrlkw/lnH\nZ0pQKDDW1xMfGyPY1HjMtpw0I3csrybWXc7Smoojj3/Us5yKSWhHu6xyJaW1i/nq9LumZAbxx8Pk\nAyecTX40KRaj/7e/JnioCeOs2aTf8cVzct1q/UX5CEB7ygxWjDYQHLEQddsZqRN53zYdRkfo+93D\nSLHYMfvplFrmZ87i6zO/xA8X/CM3llxFjimb7liQsdD7jPme4NH9T/J6x85jZp4f7d2u9fSFW1Eo\nskhzGRFFiZKqCkbXvosA7DMVMbu6YNLfA5lsosg97cOiwQHUgeTMcZVhYhI5nIy2uATWvItW6iOO\ng8d/sxkSaZSSHJoPAh0pDkiAGIphM6oRlCJjwShj4Rh+JAJA5PAfCYE8vRINCXojca5fVIjZcHYD\nkGn2XDzr38O7bSv6ymNLGy6cnkl5npXyYgeukWRJ0amchHa0UlsxpeNMWjJZUq+7Af/uBtzvrMZY\nV3/C0QcpkWDgj7/Hv2c3+qpqMu6+d0KLf0wks1VHWU0GTXtBKakoDvXT3lXFQetm4nmLyYp7KGo8\niPOZJ0m77Y5PPYZFY2ZZ3mKW5S1m0D/Ehz0b2Ny/C2+slTfaWlnb9Qpz0muZnTGTQnMegiDQ7G7l\n5dY3EdDjiM0j4guRV2Qi1rSfhC/5+Wswl/G1yonPXSCTTRY5aB825u1GETGiifoxFZyd2qofpezM\nGGujzVoBAoRVHqzBKCWhCKqhbjIuWUTagrkM9o2xd3svzgEvIlCWaWbGnFxSsk209Xlp6fXQ3OOh\na9BLPCFhNaq5dPbZT8eoKytHYbHg3bmdtNtuR1Ae+xFzWHXH5JIOtrSgMJpQpZ2dbF3nMlGjSeYm\n/+mPTzhMLkkSQ08+gXfLZrTFJWTd93VE1fErfJ0L6i/K49C+AdpTalk+vInfaVcS6MmjoMLJi96L\neEATZnTtGtSZWViXXnLCY6Ub0ri2/CYuz1nAuwdfZG1QRzjWzobeD9jQ+wF2bQr16TP4sH87kgQG\n/SW+5TEAABlwSURBVCXkd7vwo6e0KgfXa79HAnq1qUQdaeSmyUPjsvOHHLQPa+5rQ5ByMUZ6SCma\n3OVeH1Gm2MFoItfp5Z2L3iMhxkkZi3HVQT2q9l5UmVl4Ugt59and+MaSS1UKy1KZMSeXjGzzkaHQ\nFLOOWRXJ3nk4GqdzwEuKSXNkSdXZJIgipvpZjK5dQ6Dx4AkLgERdLmKuEQy1M8/JYd2poCstw3rJ\nCkbffZuRl144kkjnaCMvv4hn3RrU2Tlkf+NbR4qOnMssNj2lVekc2gdRUU99tJsdAwUMOrYTVdrZ\nUXctczY9wdATjxNzu7Ffd8NJRw40hkwurVpFStObvBa5hUSinxzdAK2jB3m7cx0AWnEWaqUDadCH\nQiGQZYnT13woOQHNXM6c8gz5syc7r5yb42lToK0/WfHFGHGTWVJ1Vs4pCAKGsjIMoQSGUAQEqG8M\nokuoQZLYoaxh8/oOQsEo1XXZ3PblOVx+QzWZOZbjftFoVArKcq2kWicvOczJmGYfm2jleEJtZ79I\nyPkg9fobUaWl435n9Sdmk7vfXo3rtVdQOdLI+dZ3UBimZsb96aibn7wYbk+p5aLBnYgJCX97MYbs\nPj7oiZL14D+gSkvH9cZr9P/m18dN73o0lc7BrIorWalpQKHIwRmdxddmPMhdVbeRKyxEbailyN9N\nwK8jr9hO4P33AAgplDQZ86kvn7yEQzLZZJCDNpBIRHGNJYcXtVEPFsfZu8d1ZIh8OIrdJaJVzSbW\n0YZTn4s/JY+5Swq54775LLq0FIvt7MwCP1Pa4hKUNhu+XTs+MbnoaB9V9prqSWjnGlGjIf2LfwMc\nzk1+OOmKZ9NGnM88icJiJeeBB1Faz85tnIlis+spnZaGT5NCQGFhhdRFwmsnoQoQiEQ4FNCQ971/\nRldRiW/XDrp/8kOirpGTHleltTOv4gpWqHcTleDPLf2kqEvwx3OTRUj6k/evi0pS8Gx4D4C95mL0\nJj3F2ZObv0Amm2hy0AaiwSHC/mSPRSSMeBYn9OiKkgGrqENPatd8Mlp2IQkiqTfdzO1fnUfd/Hy0\nunP7fuVfE0QR46w5JAIB/Pv3Hfd5odYWUCjQFpy9Ge7nC31ZOdZLlhMdGGDk5Rfw7tzB4KN/QDQY\nyHngQVSO87OHWH9RsrfdZq9lWtd2NEKEYHchYkofWw8OojAayfnbb2NZvIRwdxddP/wBwba2kx5X\nUpgpzVjMEsVewpLIY83dxC1G0qUhfCN2lEqRlNEWpMMXQLtMFdSVOSas5KdMdrbI97SBsL8PZUCP\nICVAffye4WTQ5OeDQkGmK4pJ24c+5sW6/FLSFh//XvD5wDR7DqPvrMa7bQvGGbWf2J4Ihwl1daLN\nzx/3muQLTer1N+HfvRv326sRFO8iqNVkf/MBNNnZU92002ZLNVBS6aDlIHjVdq6TOng6WoYoiOxq\nHiIcrUCjUpJ2xxdRZ2XjfPpJen72Y9Lvuht17SyG3EGG3EGco0EGD/895A7iGgshAUa1kUVzd7NL\nnfzMZQ87cfqsFJU78K75CwDdJgsutYW6stQpfCdkstMjB21g2NuJJmTFEBkF89m9Fyyq1Why87B0\nd2Hr2wMGA/arrz2rbZgM2sIilKmp+Bt2kYhGEFXHBuZQZwfE42iLpy6pyrlO1GhIv+tuen72EwCy\n7/8muilK9TqR6i7Kp+Wgk7aUmcxsewv7tGxGhjNJmIfYcmCQ7FQDQ6NBhrSlxObeSMW2lxn4v9/y\nvm0676fMgL/qHVuNakpzLKTZ9DhsOtKMEobQTkYEPUpXBhAizy4R7e0FYLe1Gr1GSUWebQpevUx2\nZuSgDbQNDCJIqRgibjQ5Z/8XWVdcQrijnUQwiOPWz59Xk4uORxAETLPm4H7rDfx792Kqqz9me+jw\nBCt5EtqJ6cvKkzPEDUZ0RUVT3ZwJYXcYKSpPpa0JxrRpXBds4/dCDVJUy6NvHgSODsp6tmav5OaB\ndSx076HSEMV76U04Us04bDocVh0alYJ4PEFPh5uWA0Ps2zJMNJKCRhPHpwGlSsTYtJkgEFKKHFAX\nMLckdVLK1Mpkk+2CD9pSIkb/SDKbkjHsxlZ49gsHaIuLYc07qDMysS5ZetbPP1lMs5NB27dtyyeC\ndvAcSapyPjDUTE15zclUf1EBbU3DtNrrqG97nYJZmXS4U0lNj1KXV0SaTZf8Y9WRYtZCYDn9D/8v\n9uaDZK39M9n3fwPRrKe3c5SWg0O0HxomHEre2jKaNUyrzSIty8zbL+6nqMRG8J0PATiQlUdCUExq\nmVqZbDJd8EE7GnIyOmZASXK5V1rp2VnudTTj9FpM8y/CunT5J5KRnM80efmo0tPx7W4gEQ4fWU8s\nSRLB1haUdjtKqzxEeSFKTTdSWJpKezN4tOmsHGznNzoLw4NKvEXdXD9jIZqj08mazGQ/8CCDf3qM\nroYW9v7iJZyWYkLhZBlYg1FN+awciisdpGclcxhsWZ+cwJYZH4R48sL8gLEOdUKkuijlrL9mmWwi\nnFaECIVCPPjgg4yMjGAwGHjooYdISTn2l+DRRx/l9ddfB2DJkiXcf//9SJLE4sWLKSgoAKC2tpZv\nf/vbZ/YKzlAk0E/Eb0QJ6MOjWDPOfhYxUasl8+57z/p5J5sgCJhmz8H12qv4dzccqe8c6usn4fNh\nmFY9xS2UTaX6Bfm0Nw/TkjqLWV2vcs0NFby6X8fmzQr2Of/IdXOrWJg1F6WoZKB3jNaDQ7QGphHI\nTo7OqAN+yvLNVC6uIjP32NwFkiTR2uhEqRLR7XgbCeh26OmJGKkvs6NRnf3EQzLZRDitoP3kk09S\nVlbG17/+dV5//XUefvhh/umf/unI9u7ubl555RWeffZZBEHgtttuY/ny5eh0Oqqqqvjtb387YS/g\nTAUDvSiCBhSJMDEV53w6yPONafZcXK+9infb1iNBe6wxWUxEHhq/sDkyTOQX2+lshVFdBsWb9/Ld\nL3+Z/35mD96WUp73NbJN0YnNnU0skNxHo1VSOSOTbJWHxItPIbSH0NhuhNyrjjn28KAPjztIQbYG\n6eAwAN0FteBGHhqXnddOK2jv2LGDe+65B4DFixfz8MMPH7M9IyODRx55BIUieTUbi8XQaDTs37+f\nwcFB7rjjDrRaLf/wD/9A0RRPrulxd6MOVWIKDxLWTV0Wsc8qTXYO6qws/Ht3Ew8GUeh0eA82AaCd\nwnKcsnPDrIX5dLaO0JI6hzndr6Bsbueaiixe2dtPYKAIHwJ6RZRwupuamnyW1tajOnwLKVyeQe//\n/IKRF58n3NuH7vpbcY2EcA766G51AZDqPABAQCPQKk5DIUaZUWKfstcrk52pkwbtZ599lscee+yY\nx+x2OyaTCQCDwYDX6z1mu0qlIiUlBUmS+OlPf8q0adMoLCxkeHiYe++9l5UrV7J9+3YefPBBnn/+\n+ROe32bTo1RO7FCWw5Fsu5SI0+0MIiBgDLvBYjiyTTZxgksW0f3k04htB3FcvITuxkZErZac2koE\nhTxMORXOlc+5w2Fid0UPLY3g1qZjeeI3pOjSudKUyzZjDj2CEWNlgh5DA21j29m+axM3TbuCEk0Z\nLsmCa+VXaN9ykFGnj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+rxhw8fKpVK6cSJE1qyZInpWM5IJBI6ePCgnj17Js/z1NLSopqa\nGtOxnPHkyRPt27dPV65c0aNHj3T48GFNTEyooqJCkUhEPp/PdMS09vb4x2IxdXR0yO/3q6CgQB0d\nHe+cusPcikQiunbtmioqKqZfO3TokCKRiJVzwJnSBgDAds4sjwMAYDtKGwAAS1DaAABYgtIGAMAS\nlDYAAJagtAEAsASlDQCAJShtAAAs8Rcd7W/587H1vAAAAABJRU5ErkJggg==\n",
                        "text/plain": [
                            "<Figure size 576x396 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "try:\n",
                "    for i in range(N_SPLITS):\n",
                "        plt.plot([c0 - c1 for c0, c1 in zip(cate.fitted_nuisances['model_T_XZ'][i].model0.coef_.flatten(),\n",
                "              cate.fitted_nuisances['model_T_XZ'][i].model1.coef_.flatten())])\n",
                "    \n",
                "    plt.title(\"Difference in coefficients betwen model0 and model1\")\n",
                "    plt.show()\n",
                "except:\n",
                "    print(\"Unavailable\")"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "# ATE via DRIV"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 130,
            "metadata": {
                "collapsed": true
            },
            "outputs": [],
            "source": [
                "from dml_iv import DMLIV\n",
                "from dr_iv import DRIV, ProjectedDRIV\n",
                "from utilities import SubsetWrapper, StatsModelLinearRegression, ConstantModel\n",
                "from sklearn.dummy import DummyRegressor\n",
                "\n",
                "np.random.seed(random_seed)\n",
                "\n",
                "# For DRIV we need a model for predicting E[T*Z | X]. We use a classifier\n",
                "model_TZ_X = lambda: model()\n",
                "\n",
                "# We also need a model for the final regression that will fit the function theta(X)\n",
                "# If we want to fit an ATE, we simply fit a constant functin theta(X) = theta\n",
                "# We can do this with a pipeline where the preprocessing step only creates a bias column\n",
                "# and the regression step fits a linear regression with no intercept.\n",
                "# To get normal confidence intervals easily we can use a statsmodels linear regression\n",
                "# wrapped in an sklearn interface\n",
                "const_driv_model_effect = lambda: ConstantModel()\n",
                "\n",
                "# As in OrthoDMLIV we need a perliminary estimator of the CATE.\n",
                "# We use a DMLIV estimator with no cross-fitting (n_splits=1)\n",
                "dmliv_prel_model_effect = DMLIV(model_Y_X(), model_T_X(), model_T_XZ(),\n",
                "                                dmliv_model_effect(), dmliv_featurizer(),\n",
                "                                n_splits=1, binary_instrument=True, binary_treatment=True)\n",
                "\n",
                "const_dr_cate = DRIV(model_Y_X(), model_T_X(), model_Z_X(), # same as in DMLATEIV\n",
                "                        dmliv_prel_model_effect, # preliminary model for CATE, must support fit(y, T, X, Z) and effect(X)\n",
                "                        model_TZ_X(), # model for E[T * Z | X]\n",
                "                        const_driv_model_effect(), # model for final stage of fitting theta(X)\n",
                "                        cov_clip=COV_CLIP, # covariance clipping to avoid large values in final regression from weak instruments\n",
                "                        n_splits=N_SPLITS, # number of splits to use for cross-fitting\n",
                "                        binary_instrument=True, # a flag whether to stratify cross-fitting by instrument\n",
                "                        binary_treatment=True # a flag whether to stratify cross-fitting by treatment\n",
                "                       )"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 131,
            "metadata": {
                "collapsed": false
            },
            "outputs": [
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": [
                        "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_split.py:652: Warning: The least populated class in y has only 8 members, which is too few. The minimum number of members in any class cannot be less than n_splits=10.\n",
                        "  % (min_groups, self.n_splits)), Warning)\n"
                    ]
                },
                {
                    "data": {
                        "text/plain": [
                            "<dr_iv.DRIV at 0x222e1dcbef0>"
                        ]
                    },
                    "execution_count": 131,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "const_dr_cate.fit(y, T, X, Z, store_final=True)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 132,
            "metadata": {
                "collapsed": false
            },
            "outputs": [
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": [
                        "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\statsmodels\\regression\\linear_model.py:1554: RuntimeWarning: divide by zero encountered in double_scalars\n",
                        "  return self.ess/self.df_model\n"
                    ]
                },
                {
                    "data": {
                        "text/html": [
                            "<table class=\"simpletable\">\n",
                            "<caption>OLS Regression Results</caption>\n",
                            "<tr>\n",
                            "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>  -0.000</td> \n",
                            "</tr>\n",
                            "<tr>\n",
                            "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>  -0.000</td> \n",
                            "</tr>\n",
                            "<tr>\n",
                            "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>    -inf</td> \n",
                            "</tr>\n",
                            "<tr>\n",
                            "  <th>Date:</th>             <td>Sat, 01 Jun 2019</td> <th>  Prob (F-statistic):</th>  <td>   nan</td>  \n",
                            "</tr>\n",
                            "<tr>\n",
                            "  <th>Time:</th>                 <td>16:53:02</td>     <th>  Log-Likelihood:    </th> <td> -5935.3</td> \n",
                            "</tr>\n",
                            "<tr>\n",
                            "  <th>No. Observations:</th>      <td>  2991</td>      <th>  AIC:               </th> <td>1.187e+04</td>\n",
                            "</tr>\n",
                            "<tr>\n",
                            "  <th>Df Residuals:</th>          <td>  2990</td>      <th>  BIC:               </th> <td>1.188e+04</td>\n",
                            "</tr>\n",
                            "<tr>\n",
                            "  <th>Df Model:</th>              <td>     0</td>      <th>                     </th>     <td> </td>    \n",
                            "</tr>\n",
                            "<tr>\n",
                            "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
                            "</tr>\n",
                            "</table>\n",
                            "<table class=\"simpletable\">\n",
                            "<tr>\n",
                            "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
                            "</tr>\n",
                            "<tr>\n",
                            "  <th>const</th> <td>    0.0718</td> <td>    0.032</td> <td>    2.231</td> <td> 0.026</td> <td>    0.009</td> <td>    0.135</td>\n",
                            "</tr>\n",
                            "</table>\n",
                            "<table class=\"simpletable\">\n",
                            "<tr>\n",
                            "  <th>Omnibus:</th>       <td>1560.900</td> <th>  Durbin-Watson:     </th>  <td>   1.967</td>  \n",
                            "</tr>\n",
                            "<tr>\n",
                            "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>3111172.573</td>\n",
                            "</tr>\n",
                            "<tr>\n",
                            "  <th>Skew:</th>           <td> 0.819</td>  <th>  Prob(JB):          </th>  <td>    0.00</td>  \n",
                            "</tr>\n",
                            "<tr>\n",
                            "  <th>Kurtosis:</th>       <td>160.992</td> <th>  Cond. No.          </th>  <td>    1.00</td>  \n",
                            "</tr>\n",
                            "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
                        ],
                        "text/plain": [
                            "<class 'statsmodels.iolib.summary.Summary'>\n",
                            "\"\"\"\n",
                            "                            OLS Regression Results                            \n",
                            "==============================================================================\n",
                            "Dep. Variable:                      y   R-squared:                      -0.000\n",
                            "Model:                            OLS   Adj. R-squared:                 -0.000\n",
                            "Method:                 Least Squares   F-statistic:                      -inf\n",
                            "Date:                Sat, 01 Jun 2019   Prob (F-statistic):                nan\n",
                            "Time:                        16:53:02   Log-Likelihood:                -5935.3\n",
                            "No. Observations:                2991   AIC:                         1.187e+04\n",
                            "Df Residuals:                    2990   BIC:                         1.188e+04\n",
                            "Df Model:                           0                                         \n",
                            "Covariance Type:            nonrobust                                         \n",
                            "==============================================================================\n",
                            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
                            "------------------------------------------------------------------------------\n",
                            "const          0.0718      0.032      2.231      0.026       0.009       0.135\n",
                            "==============================================================================\n",
                            "Omnibus:                     1560.900   Durbin-Watson:                   1.967\n",
                            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):          3111172.573\n",
                            "Skew:                           0.819   Prob(JB):                         0.00\n",
                            "Kurtosis:                     160.992   Cond. No.                         1.00\n",
                            "==============================================================================\n",
                            "\n",
                            "Warnings:\n",
                            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
                            "\"\"\""
                        ]
                    },
                    "execution_count": 132,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "# To get the statsmodel summary we look at the effect_model, which is the pipeline, we then look\n",
                "# at the reg step of the pipeline which is the statsmodel wrapper and then we look\n",
                "# at the model attribute of the statsmodel wrapper and print the summary()\n",
                "const_dr_cate.effect_model.summary()"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "#### Some diagnostics of the fitted nuisance models across folds"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 133,
            "metadata": {
                "collapsed": false
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "Model prel_model_effect max std of coefs: 0.08861170536431623\n",
                        "Model model_TZ_X max std of coefs: 0.1551255726963632\n",
                        "Model model_T_X max std of coefs: 0.07953278250754123\n",
                        "Model model_Z_X max std of coefs: 0.014391208119373949\n",
                        "Model model_Y_X max std of coefs: 0.026554847406610734\n"
                    ]
                }
            ],
            "source": [
                "# The property .fitted_nuisances is a dictionary of the form:\n",
                "# {'name_of_nuisance': [fitted_model_fold_1, fitted_model_fold_2, ...]}\n",
                "# then we can access all properties of each of the fitted models for each fold.\n",
                "# If for instance all nuisances have a linear form we can look at the standard deviation\n",
                "# of the coefficients of each of the nuisance model across folds to check for stability\n",
                "try:\n",
                "    [print(\"Model {} max std of coefs: {}\".format(name,\n",
                "                                                  np.max(np.std([ns.coef_ for ns in nuisance_insts], axis=0)))) \n",
                "     if hasattr(nuisance_insts[0], 'coef_') else None\n",
                "     for name, nuisance_insts in const_dr_cate.fitted_nuisances.items()]\n",
                "except:\n",
                "    print(\"Unavailable\")"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 134,
            "metadata": {
                "collapsed": false
            },
            "outputs": [
                {
                    "data": {
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gw98JxMsBqBkH6RXgarK4BKElDJpAvL29+f777wGYMGGC5vHarln/VLUFZB7ONthZ17Rz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\nJBBBEHQmEoggCDr7/xN/tPLENyndAAAAAElFTkSuQmCC\n",
                        "text/plain": [
                            "<Figure size 288x216 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "image/png": 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Xrjg72883260uJmTz99W/UFhchvZGQrCkI2dTee/Z/nY76O1y8o3+jzb2cfly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\n/wHEJBEPI1T/NQAAAABJRU5ErkJggg==\n",
                        "text/plain": [
                            "<Figure size 288x216 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "image/png": 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s27atxefZk6tWQ3SoL2kXypxuyRB7KdVVk1tYQWy4X6OL3NlKsJ873h5aUh2k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\nMklJkuTQZJKSJMmhySQlSZJD+/9D7I/B3DHIIAAAAABJRU5ErkJggg==\n",
                        "text/plain": [
                            "<Figure size 288x216 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                },
                {
                    "data": {
                        "image/png": 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YiFFYWN70E3UIjZk9+hg31zE7ODS+CLQhBnOlIxS+uKJZ19GY2aOPcetjzIABJR2KonQD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\n/wMZWwLeQjNrEAAAAABJRU5ErkJggg==\n",
                        "text/plain": [
                            "<Figure size 288x216 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "try:\n",
                "    nuisance_diagnostic(const_dr_cate, 'model_TZ_X', 'coef', lambda ns: ns.coef_.flatten(), X_df.columns)\n",
                "    nuisance_diagnostic(const_dr_cate, 'model_TZ_X', 'predict', lambda ns: ns.predict(X), np.arange(X.shape[0]))\n",
                "    nuisance_diagnostic(const_dr_cate, 'model_T_X', 'coef', lambda ns: ns.coef_.flatten(), X_df.columns)\n",
                "    nuisance_diagnostic(const_dr_cate, 'prel_model_effect', 'effect', lambda ns: ns.effect(X), np.arange(X.shape[0]))\n",
                "except:\n",
                "    print(\"Unavailable\")"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Projecting CATE to a pre-chosen subset of variables in final model"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 135,
            "metadata": {
                "collapsed": false
            },
            "outputs": [
                {
                    "name": "stdout",
                    "output_type": "stream",
                    "text": [
                        "[4]\n"
                    ]
                }
            ],
            "source": [
                "from dml_iv import DMLIV\n",
                "from dr_iv import DRIV, ProjectedDRIV\n",
                "from utilities import SubsetWrapper\n",
                "\n",
                "np.random.seed(random_seed)\n",
                "\n",
                "# We could also fit a projection on a subset of the features by using the\n",
                "# subset wrapper from our utilities.\n",
                "\n",
                "# Example: including everything for expository purposes, but any array_like of indices would work\n",
                "subset_names = set(['motheduc'])\n",
                "# list of indices of features X to use in the final model\n",
                "feature_inds = np.argwhere([(x in subset_names) for x in X_df.columns.values]).flatten()\n",
                "print(feature_inds)\n",
                "# Because we are projecting to a low dimensional model space, we can\n",
                "# do valid inference and we can use statsmodel linear regression to get all\n",
                "# the hypothesis testing capability\n",
                "proj_driv_model_effect = lambda: SubsetWrapper(StatsModelLinearRegression(),\n",
                "                                          feature_inds # list of indices of features X to use in the final model\n",
                "                                         )"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 136,
            "metadata": {
                "collapsed": true
            },
            "outputs": [],
            "source": [
                "proj_dr_cate = const_dr_cate.refit_final(proj_driv_model_effect())"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 137,
            "metadata": {
                "collapsed": true
            },
            "outputs": [],
            "source": [
                "# To get the CATE at every X we call effect(X[:, feature_inds])\n",
                "proj_dr_effect = proj_dr_cate.effect(X[:, feature_inds])"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 138,
            "metadata": {
                "collapsed": false
            },
            "outputs": [
                {
                    "data": {
                        "text/html": [
                            "<table class=\"simpletable\">\n",
                            "<caption>OLS Regression Results</caption>\n",
                            "<tr>\n",
                            "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   0.002</td> \n",
                            "</tr>\n",
                            "<tr>\n",
                            "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.002</td> \n",
                            "</tr>\n",
                            "<tr>\n",
                            "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   6.316</td> \n",
                            "</tr>\n",
                            "<tr>\n",
                            "  <th>Date:</th>             <td>Sat, 01 Jun 2019</td> <th>  Prob (F-statistic):</th>  <td>0.0120</td>  \n",
                            "</tr>\n",
                            "<tr>\n",
                            "  <th>Time:</th>                 <td>16:53:03</td>     <th>  Log-Likelihood:    </th> <td> -5932.2</td> \n",
                            "</tr>\n",
                            "<tr>\n",
                            "  <th>No. Observations:</th>      <td>  2991</td>      <th>  AIC:               </th> <td>1.187e+04</td>\n",
                            "</tr>\n",
                            "<tr>\n",
                            "  <th>Df Residuals:</th>          <td>  2989</td>      <th>  BIC:               </th> <td>1.188e+04</td>\n",
                            "</tr>\n",
                            "<tr>\n",
                            "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>    \n",
                            "</tr>\n",
                            "<tr>\n",
                            "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
                            "</tr>\n",
                            "</table>\n",
                            "<table class=\"simpletable\">\n",
                            "<tr>\n",
                            "      <td></td>        <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
                            "</tr>\n",
                            "<tr>\n",
                            "  <th>const</th>    <td>    0.0726</td> <td>    0.032</td> <td>    2.257</td> <td> 0.024</td> <td>    0.010</td> <td>    0.136</td>\n",
                            "</tr>\n",
                            "<tr>\n",
                            "  <th>motheduc</th> <td>   -0.0815</td> <td>    0.032</td> <td>   -2.513</td> <td> 0.012</td> <td>   -0.145</td> <td>   -0.018</td>\n",
                            "</tr>\n",
                            "</table>\n",
                            "<table class=\"simpletable\">\n",
                            "<tr>\n",
                            "  <th>Omnibus:</th>       <td>1570.882</td> <th>  Durbin-Watson:     </th>  <td>   1.972</td>  \n",
                            "</tr>\n",
                            "<tr>\n",
                            "  <th>Prob(Omnibus):</th>  <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>3062292.770</td>\n",
                            "</tr>\n",
                            "<tr>\n",
                            "  <th>Skew:</th>           <td> 0.842</td>  <th>  Prob(JB):          </th>  <td>    0.00</td>  \n",
                            "</tr>\n",
                            "<tr>\n",
                            "  <th>Kurtosis:</th>       <td>159.746</td> <th>  Cond. No.          </th>  <td>    1.01</td>  \n",
                            "</tr>\n",
                            "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
                        ],
                        "text/plain": [
                            "<class 'statsmodels.iolib.summary.Summary'>\n",
                            "\"\"\"\n",
                            "                            OLS Regression Results                            \n",
                            "==============================================================================\n",
                            "Dep. Variable:                      y   R-squared:                       0.002\n",
                            "Model:                            OLS   Adj. R-squared:                  0.002\n",
                            "Method:                 Least Squares   F-statistic:                     6.316\n",
                            "Date:                Sat, 01 Jun 2019   Prob (F-statistic):             0.0120\n",
                            "Time:                        16:53:03   Log-Likelihood:                -5932.2\n",
                            "No. Observations:                2991   AIC:                         1.187e+04\n",
                            "Df Residuals:                    2989   BIC:                         1.188e+04\n",
                            "Df Model:                           1                                         \n",
                            "Covariance Type:            nonrobust                                         \n",
                            "==============================================================================\n",
                            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
                            "------------------------------------------------------------------------------\n",
                            "const          0.0726      0.032      2.257      0.024       0.010       0.136\n",
                            "motheduc      -0.0815      0.032     -2.513      0.012      -0.145      -0.018\n",
                            "==============================================================================\n",
                            "Omnibus:                     1570.882   Durbin-Watson:                   1.972\n",
                            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):          3062292.770\n",
                            "Skew:                           0.842   Prob(JB):                         0.00\n",
                            "Kurtosis:                     159.746   Cond. No.                         1.01\n",
                            "==============================================================================\n",
                            "\n",
                            "Warnings:\n",
                            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
                            "\"\"\""
                        ]
                    },
                    "execution_count": 138,
                    "metadata": {},
                    "output_type": "execute_result"
                }
            ],
            "source": [
                "# To get the statsmodel summary we look at the effect_model, which is\n",
                "# an instance of SubsetWrapper, we look at the model of the SubsetWrapper which is \n",
                "# and instance of the pipeline, we then look at the reg step of the pipeline which is the statsmodel wrapper and\n",
                "# call summary() of the wrapper (most prob there is a better API for this, but we can go with this for now :)\n",
                "proj_dr_cate.effect_model.summary(alpha=.05, xname=['const']+list(X_df.columns[feature_inds]))"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 139,
            "metadata": {
                "collapsed": false
            },
            "outputs": [
                {
                    "data": {
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3oRPBx2I4vv8qDs6W1Kmrv5yLFWHyomPFHu/9jCfdW3uQk6dm0sKjZOdpMJHLsLVU6Vrs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\nwAgI4QkERuD/AVt7uYaEzwP9AAAAAElFTkSuQmCC\n",
                        "text/plain": [
                            "<Figure size 216x288 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "# We can also evaluate coverage and create prediction intervals using statsmodels attributes\n",
                "import statsmodels.api as sm\n",
                "from statsmodels.sandbox.regression.predstd import wls_prediction_std\n",
                "res = proj_dr_cate.effect_model.model\n",
                "predictions = res.get_prediction(PolynomialFeatures(degree=1, include_bias=True).fit_transform(X[:, feature_inds]))\n",
                "frame = predictions.summary_frame(alpha=0.05)\n",
                "pred = frame['mean']\n",
                "iv_l = frame['mean_ci_lower']\n",
                "iv_u = frame['mean_ci_upper']\n",
                "\n",
                "fig, ax = plt.subplots(figsize=(3,4))\n",
                "order = np.argsort(X[:, feature_inds[0]])\n",
                "ax.plot(X[order, feature_inds[0]], iv_u[order], 'C3--', label=\"Upper 95% CI\")\n",
                "ax.plot(X[order, feature_inds[0]], iv_l[order], 'C3--', label=\"Lower 95% CI\")\n",
                "ax.plot(X[order, feature_inds[0]], pred[order], 'C0--.', label=\"Prediction\")\n",
                "ax.legend(loc='best')\n",
                "plt.xlabel(\"Mother's Education (scaled)\")\n",
                "plt.ylabel(\"Treatment Effect\")\n",
                "#plt.savefig(\"NLSYM_momeduc_linear_projection_2.pdf\", dpi=300, bbox_inches='tight')\n",
                "plt.show()"
            ]
        },
        {
            "cell_type": "markdown",
            "metadata": {},
            "source": [
                "## Random Forest Based CATE and Tree Explainer"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 140,
            "metadata": {
                "collapsed": true
            },
            "outputs": [],
            "source": [
                "from dml_iv import DMLIV\n",
                "from dr_iv import DRIV, ProjectedDRIV\n",
                "from utilities import SubsetWrapper\n",
                "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
                "\n",
                "np.random.seed(random_seed)\n",
                "\n",
                "rf_driv_model_effect = lambda: RandomForestRegressor(n_estimators=5000, max_depth=2, min_impurity_decrease=0.01,\n",
                "                                                     min_samples_leaf=100, bootstrap=True)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 141,
            "metadata": {
                "collapsed": true
            },
            "outputs": [],
            "source": [
                "rf_dr_cate = const_dr_cate.refit_final(rf_driv_model_effect())"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 142,
            "metadata": {
                "collapsed": false
            },
            "outputs": [
                {
                    "name": "stderr",
                    "output_type": "stream",
                    "text": [
                        "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6521: MatplotlibDeprecationWarning: \n",
                        "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
                        "  alternative=\"'density'\", removal=\"3.1\")\n"
                    ]
                },
                {
                    "data": {
                        "image/png": 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eqz5CgzTIgwEnLxXiweh2sNl5FJRUQME5EiDdO4Ti6q1y5BUYwKlVyMvXu0UY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\nQYgACYsgRICERRAiQMIiCBEgYRGECPx/ZTLnqqz8N3cAAAAASUVORK5CYII=\n",
                        "text/plain": [
                            "<Figure size 216x288 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "import seaborn as sns\n",
                "\n",
                "rf_dr_effect = rf_dr_cate.effect(X)\n",
                "plt.figure(figsize=(3,4))\n",
                "sns.distplot(rf_dr_effect)\n",
                "plt.xlabel(\"Treatment Effect\")\n",
                "plt.ylabel(\"Frequency\")\n",
                "plt.savefig(\"NLSYM_hte_distribution_2.pdf\", dpi=300, bbox_inches='tight')\n",
                "plt.show()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 143,
            "metadata": {
                "collapsed": false
            },
            "outputs": [
                {
                    "data": {
                        "image/png": 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jM6zZ6mx73U7FoYiIyF4Mq3YxboDzw2c0YfPupignHOLdLQnUnY3PKI9k0L4o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\nAAAAAElFTkSuQmCC\n",
                        "text/plain": [
                            "<Figure size 1440x216 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "import shap\n",
                "import pandas as pd\n",
                "\n",
                "Xdf = pd.DataFrame(X, columns=X_df.columns)\n",
                "# explain the model's predictions using SHAP values\n",
                "explainer = shap.TreeExplainer(rf_dr_cate.effect_model)\n",
                "shap_values = explainer.shap_values(Xdf)\n",
                "\n",
                "# visualize the first prediction's explanation (use matplotlib=True to avoid Javascript)\n",
                "shap.force_plot(explainer.expected_value, shap_values[0,:], Xdf.iloc[0,:], matplotlib=True)"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 183,
            "metadata": {
                "collapsed": false
            },
            "outputs": [
                {
                    "data": {
                        "image/png": 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M0dSeKC2+m2C2l5HmBZwzuuFi+I+cP4MltEb+GZj1NfX3NxzB/ebvlL5d7WUtgZx3eAaA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\n",
                        "text/plain": [
                            "<Figure size 288x288 with 2 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "plt.figure(figsize=(4,4))\n",
                "shap.summary_plot(shap_values, Xdf, plot_type=\"violin\", max_display=10, show=False, auto_size_plot=False)\n",
                "plt.savefig(\"NLSYM_shap_summary_violin_2.pdf\", dpi=300, bbox_inches='tight')\n",
                "plt.show()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": 145,
            "metadata": {
                "collapsed": false
            },
            "outputs": [
                {
                    "data": {
                        "image/png": 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NyKp73rNnz0fqni9evJjatWtrdc+Bx7b3pLrnQghh6KQY1DMqyLrnUsO8YBlq\n3GC4sUvcBUuKQQkhhCgSXshpq6JOTdAZ5LcbIcSLQ5LHM3J3d8fd3b2wwxBCiCJBpq2EEELoTZKH\nEEIIvUnyEEIIoTdJHkIIIfQmF8yLoCeVoZUSs0KIokJGHkIIIfQmyUMIIYTeJHk8g8uXLzNo0CBs\nbGzw8PAgODhY25aYmMj06dOxt7fH2dmZVatWadsSEhKYNWsWLi4uODk5MWPGDOLj4wvjFIQQIk9J\n8niKlJQURo0ahaOjI2FhYcyaNQsfHx8iIyMB8PHxAWDPnj0EBgYSHBzM3r17AViyZAnJycl8++23\nBAUFkZiYmGOVXiGEMFTF9oJ5REQEvXv3xt7entDQUCZOnMjZs2c5cOAASinat2/P8OHDMTExIT09\nnSVLlrB3717Kly9P165dWbFiBSdOnCAsLIzKlSvj5eUFQNOmTdmwYQPlypUjOjqaX375heDgYEqV\nKkX16tX57LPPMDU1BbIKQn3wwQeULVsWgC5durBkyZJC6xMhhMgrxXrkkZSUhKWlJcHBwZw4cYJr\n166xefNmNm/ezNmzZ/niiy8A+Pzzz/nzzz/Ztm0bX3zxhbYEO8D58+epU6cOPj4+ODo64uXlRVRU\nFGXKlOHChQtYWlqyfft2OnXqhLu7O8HBwVSpUgWAOXPm0LBhQ62tn3/+mfr16xdsJwghRH5QxdTN\nmzdVs2bN1PXr11VmZqZq06aNOnfunLb9xIkTytXVVSmlVOfOndVPP/2kbTt8+LBq1qyZUkqpjz/+\nWLVo0ULt3LlTpaWlqZ9++km1bdtW/fPPP2rXrl2qRYsWytfXV92/f19dvnxZdejQQe3ateuReAID\nA5Wtra26evXqU2PHL+2x/4QQoqgottNW2SpXrszdu3dJTU1l8ODBWjlYpRTp6emkpqYSHR1NtWrV\ntH0sLS21/5uamtKwYUM6deoEgJ2dHW+88QaHDx+mcuXKZGZmMnLkSEqVKkW9evXw9PQkNDSUjh07\nAllTV0uXLiUkJITPPvuMunXrPve5FPWVdg291oEhMtTYJe6CJTXMn0OJEiUwNzfHxMSEL7/8klq1\nagFw//59YmNjKVmyJNWqVSMyMpLXXnsNyKo/nq1OnTocO3YsR5uZmZkopahTpw5KKZKSkjAzM8ux\nDSA1NZXJkycTFRXF//73vxxJSQghDFmxvuaRzdjYmPbt27Ny5UoSEhK4f/8+8+fPZ/bs2QC4ubmx\nfv16YmJiiIuLY926ddq+jo6OxMTEsHnzZjIzMwkNDeXcuXPY2tpSv359GjVqxCeffEJKSgpXr14l\nKCgIZ2dnAObPn8+dO3f4/PPd4amIAAAgAElEQVTPJXEIIYqVYj/yyDZhwgRWrFhBz549SUlJwcrK\nigULFgDQr18/bt26Rbdu3ahQoQK2tracPn0agKpVq7J69WoWL17M6tWrqVq1Kr6+vloyWL58OX5+\nfri5uWFsbIyXlxcuLi5ER0eza9cuTE1Nad++vRZHhQoV2LFjR8F3gBBC5CGpYQ6cOXOG2rVrU758\neQB++eUX5syZo/29RkF7Ug3zor62laHPBxsiQ41d4i5YUsM8n/zwww/4+fnx4MEDEhIS2Lx5M61b\nty7ssIQQosh6YaatcjNs2DDmzZtHhw4dUEphY2PD+PHjCy0eqWEuhCjqJHmQdR1Clg0RQohnJ9NW\nQggh9CbJQwghhN4keQghhNCbJA8hhBB6k+RRBJVYnI7FhkdrmAshRFEhyUMIIYTeJHkIIYTQm/yd\nxzO4fPkyvr6+nD9/nkqVKjF8+HBcXFyArBrmvr6+/PLLL+h0Ojw9PRk+fDgANjY2OdpJT09HKcXR\no0cL/ByEECIvSfJ4iuwa5t7e3qxZs4ZTp04xcuRIGjduTPXq1fHx8aFkyZLs2bOHuLg4Bg8ezCuv\nvEL79u05ePCg1s79+/fp168f7777biGejRBC5I1imzyKQg3zh3366afUrl2bLl26FHRXCCFEnivW\n1zwKu4Z5tuvXr/Pdd98xceLEAj1/IYTIN4VWADefFaUa5nPmzFHz5s175tilZrkQoqgrttNW2Qq7\nhnlqairBwcGsXbtW79gNbWVdQ691YIgMNXaJu2BJDfPnUJg1zAFOnDhBlSpVaNiwYX6fqhBCFJhi\nfc0jW2HVMIesKoVvvfVWQZ+yEELkqxcieUBWDfMKFSrQs2dPOnbsSGJiYo4a5g0aNKBbt27079+f\nRo0aodNlDcqya5jv27cPe3t7Vq5c+UgN88zMTNzc3BgyZAi9evXS/gYEsu76+vcFdCGEMHRSw5yi\nW8O8qNcs/zdDnw82RIYau8RdsKSGeT6RGuZCCKEfSR5k1TBPSUmhQ4cOeHh4ULly5UKvYW5oow4h\nxIul2N9t9SykhrkQQuhHRh5CCCH0JslDCCGE3iR5CCGE0JskDyGEEHqT5FEEZf+dhxBCFFWSPB7j\n3r17JCYmFnYYQghRZEnyeIyuXbsSGRkJwJo1a5g0adITX3v58mUGDRqEjY0NHh4eBAcHa9sSExOZ\nPn069vb2ODs7s2rVqnyPXQghCoIkj8e4d+/eM70uu0Sto6MjYWFhzJo1Cx8fHy3x+Pj4ALBnzx4C\nAwMJDg4utCVPhBAiLxWL5OHv74+rqyvOzs6MHDmS8PBwkpOTWbhwIa6urri6ujJnzhxtKurfo4lL\nly5hbW0NQN++fQHo378/oaGhQFYymTRpEg4ODnh6enL06FGAHCVqjYyMHluidsqUKTlK1GYfRwgh\nDJnBJ4/jx4+zb98+tm7dyp49e7CwsCAgIIB58+Zx7do1tmzZwrZt24iNjWXevHlPbW/Tpk0AbNiw\nATs7OwBOnTqFu7s7ISEhODk5sXDhQiBvStQKIYQhMvjkUbZsWe7cuUNQUBDh4eFMmzaNjz76iP37\n9zNq1CgqVqxI+fLlGTNmDCEhIaSkpOh9jKZNm2JjY4ORkRFOTk5EREQAEB8fT3BwMNbW1vz4448M\nGTKEyZMnc+PGDeLj4wkPDycqKopvvvmG5cuX8/XXX7N79+687gIhhChwBr+21euvv87s2bPZtm0b\nq1evpkaNGgwaNIj09PQc5WQtLS1RShEdHa33McqV+79ljHU6HRkZGcB/L1Gbm7xcOrkgSdwFz1Bj\nl7gLVl7HbfDJIzIykjp16hAQEEBycjJff/01c+fOxdjYmIiICCpUqABkFWUyMjKiQoUKGBsbk5aW\nprXxrBfI/+2/lqjNjSHXDDA0hho3GG7sEnfBknoej3HmzBnGjh1LeHg4ZmZmlCtXDnNzc9zc3Fix\nYgVxcXHEx8ezfPly2rZtS7ly5ahduzZ//PEH4eHhJCYm8tVXX+Vo08TEhKSkpKce+7+WqBVCCENl\n8CMPJycnLl26xAcffEBSUhJ169Zl4cKF1KtXD39/f3r16sWDBw+wtbXVanTY29tz+PBh+vfvj5mZ\nGQMGDCAsLExr093dnWHDhjF16tRcj51donbx4sWsXr2aqlWrPlKi1s/PDzc3N4yNjfHy8spRolYI\nIQyVlKEtgkosTjfIYlCGPqQ3RIYau8RdsGTaSgghRJEgyUMIIYTeJHkUQWqCwV+KEkIUc5I8hBBC\n6E2ShxBCCL1J8hBCCKE3SR5CCCH0JslDCCGE3iR5CCGE0JskDyGEEHqT5CGEEEJvkjyEEELoTZKH\nEEIIvUnyEEIIoTdJHkIIIfQm9TyEEELoTUYeQggh9CbJQwghhN4keQghhNCbJA8hhBB6k+QhhBBC\nb5I8hBBC6E2KZeezvXv3sm7dOtLT0+nduzc9e/bMsf3ChQvMnTuXpKQkmjRpwtSpU9HpdERGRjJj\nxgzu3LlDnTp1mDt3LmZmZiQkJDB9+nRu3rxJxYoVWbBgAVWqVDGI2H/77TcmTZpEtWrVAGjYsCGz\nZs0qMnFn++yzzzAyMmLw4MEABdbneR13QfX3f4n91KlTLF26lPT0dMzNzZk5cyaWlpZFvs+fFHdR\nf4///vvvLFmyhLS0NGrUqIGPjw/ly5d/vv5WIt9ERUUpd3d3FRcXp5KTk5WXl5e6fPlyjtf06NFD\nnT59WimllI+Pj9q2bZtSSqnRo0ervXv3KqWUWrt2rVq+fLlSSilfX1+1fv16pZRSO3fuVFOmTDGY\n2AMDA9UXX3yRL/HmRdwJCQnKx8dHtWnTRq1evVp7fUH0eX7EXRD9/V9jd3NzU3///bdSSqmgoCA1\nduxYpVTR7/MnxV3U3+MeHh7aa/39/dXKlSuVUs/X3zJtlY+OHz+OtbU15ubmlC5dGkdHR/bv369t\nv3XrFqmpqbz11lsAuLu7ExISQnp6Or///juOjo4AuLm5afv98ssvtG/fHgBXV1cOHz5Menq6QcT+\n119/cfToUby8vBg7diyRkZFFJm6A0NBQateuTd++fXO0WRB9nh9xF0R//5fYHzx4wNChQ6lfvz4A\n9evX12Isyn2eW9xF/T2+fft26tWrR3p6Ordv36ZcuXLA8/W3JI98FB0dnWPoV6VKFW7fvv3U7XFx\ncZQpU0abkqhSpQpRUVGP7KPT6ShTpgx37941iNjLlStHr1692LJlC23btuWjjz4qMnFDVqJ77733\nMDLK+WtREH2eH3EXRH//l9hNTU3p2LEjAJmZmQQEBGBnZ/fIPkWtz3OLu6i/x3U6HZcuXaJjx478\n9ttvuLi4PLLPs/a3JI98lJmZSYkSJbTHSqkcj5+0/d/PA9oHg/rXajL/brMox/7RRx/h4OAAQPfu\n3bly5QqJiYlFIu7cFESf50fcBdHfzxLb07anpaUxffp0MjIyGDhwoPaahxXFPn9c3IbwHn/11VcJ\nDg7m/fff15Lb8/S3JI98VK1aNWJiYrTHsbGxVK1a9anbK1WqRGJiIhkZGQDExMRo+1lYWBAbGwtA\neno6ycnJVKhQocjHnpmZybp167TnsxkbGxeJuHNTEH2e13EXVH8/S2y5bU9OTmbkyJFkZGSwZMkS\nbcRa1Pv8cXEX9fd4amoqoaGh2vMdO3bk4sWLwPP1tySPfNSiRQt+/fVX7t69S0pKCgcOHKB169ba\ndktLS0xNTTl16hQAu3fvpk2bNuh0OqysrNi3bx8Au3btok2bNgC0bduWXbt2AbBv3z6srKxy3HFT\nVGM3MjIiNDSUAwcOALBz507efPNNSpcuXSTizk1B9Hlex11Q/f1fY58xYwYvvfQSCxYswNTUVNun\nqPf54+Iu6u9xnU7HwoULOXfuHPB//QrP2d/6X+sX+tizZ4/q0aOH6tKli/rf//6nlFJq5MiR6q+/\n/lJKKXXhwgXl7e2tunbtqj766COVmpqqlFIqIiJCDRo0SHXv3l2NGDFC3bt3TymlVFxcnBozZozq\n0aOHGjBggLp586bBxH7p0iU1YMAA1aNHD/Xhhx+qW7duFam4s61evTrHXUsF1ed5HXdB9ffzxn7u\n3DnVrFkz1aNHD9W7d2/Vu3dvNXLkSKVU0e7z3OIu6u/x33//XfXp00f17t1bjRo1SkVGRiqlnq+/\nZUl2IYQQepNpKyGEEHqT5CGEEEJvkjyEEELoTZKHEEIIvUnyEEIIoTdJHkLko6FDh3LhwgUAHBwc\nCA8PB7L+EGvp0qU4ODhgZWWFjY0NM2fO5N69e9q+DRs25O+//36kzZYtW3Ls2LEcz23bto2GDRuy\nZ8+eHM+Hh4fTsGFDmjRpov1r3rw5I0aM0JaNyQvZx0lKSvpP7Rw7dgxvb28A4uPj6dOnD6mpqXkR\noshjkjyEyCc7d+6kQoUKNGzY8JFtn376KceOHSMwMJBTp06xfft2bt26xeTJk5/rWF9//TXdu3dn\n06ZNj91+6NAhfv/9d37//Xd+/vlnTE1NGTVq1HMdq6CUL18eFxcXPv3008IORTyGJA9R7ISHh9Oy\nZUvWr19P69atadmyJdu2bWPNmjW0atWKtm3bsmPHDu31v/76K926dcPa2poePXpw+vRpbduRI0fw\n8vKiVatWNG3alFGjRnH//n0AvL29+eSTT/Dw8KBp06b07dtXG1kopfj000/p3bv3Y2P8888/adOm\nDTVr1gSylpSYOnWqVgdCH+fPn+eff/5h6tSpXLhwgfPnz+f6+tKlS9O5c+fHjmq2bt1Kt27dcjzX\np08fNm/eTGZmJsuWLaN9+/Y0adIEW1tbtmzZ8kgbjxuFdO3alW+//RaAuLg4Jk6cSOvWrXFwcCAg\nIOCRtZUe3m/r1q3Ex8c/tR9EwZLkIYqluLg4bt68yc8//8z48eOZNWsWd+7c4eDBgwwfPpy5c+cC\nEBERweDBgxk6dChHjx5l4MCBDBo0iLi4OJKTkxkxYgSDBg3i6NGj7N69mzNnzrBz507tOLt27WLl\nypWEhYWhlCIgIACAkydPkpycTOPGjR8bX4cOHfj888+ZOnUqu3btIjIyknr16uHj45PjdV5eXlhb\nW+f49/DUFmR94Ht6elK2bFk8PDyeOPrIdvv2bbZs2ULLli0fG9fFixf5559/gKzlvf/88086dOjA\nDz/8QHBwMIGBgZw8eZLx48czf/58vaeqJk2aRIkSJdi/fz8bN27khx9+0BLLv5UrV4633377kek4\nUfgkeYhia8CAAZiYmNCqVSsyMjK0xzY2NsTFxXH//n127txJy5YtcXJyQqfT0aFDBxo0aMCPP/5I\nyZIl+e6773B0dCQhIYHbt29ToUKFHNcKOnfuzEsvvUS5cuVwdnbm2rVrAJw4ceKJiQOyvlEHBASQ\nmprK3LlzsbW1pXPnzhw5ciTH67Zs2cKJEydy/DM3N9e2p6SksHPnTq2SnJeXFzt37nwkwdja2mJt\nbU2zZs3o1q0bZcqU0RLow8qXL4+9vb22ztHOnTt55513qFChAk5OTmzYsEFbZr9kyZKkpqY+cqzc\nREdH8/PPPzN16lTMzMyoVasW77//Ptu2bXviPm+++SbHjx9/5mOIgiFlaEWxlf0hm70kfHbhm+yl\npjMzM4mIiODgwYNYW1tr+6Wnp9OsWTOMjY05cOAAGzZsALIuYN+/fz/HFEulSpW0/+t0Om1bZGTk\nU1frbd26tbag3eXLl9m8eTODBw8mJCQECwuLZzrH3bt3k5CQQL9+/bTnUlJS2L59O++//772XFhY\nGGXKlHmmNj09PVm6dClDhw5l586djBgxAshagnzu3LkcOXIES0tLXnvtNSCrH5/VrVu3UErh7Oys\nPZeZmZnrCq5Vq1Z95AYBUfgkeYhi61nqP1StWpWOHTuyaNEi7bkbN25QsWJFTp48yapVq9i2bRt1\n69YFyPEh/bRjP+lDNSMjg5YtW+Lv76+t0vrKK68wbdo0goKCuHLlyjMnj6+//poJEybg4eGhPbd7\n9242btzIgAEDnqmNf7OxsWHatGns37+fW7duYWtrC8DSpUtRSnHw4EFKlixJREQE33333SP7Zy9B\nnpaWpj0XFxcHZPW3Tqfj8OHD2mq09+7dy3XqKyMj45FCV6LwyU9EvNA6derETz/9xJEjR1BK8dtv\nv9G5c2f+/PNPEhMTMTIyolSpUmRkZBAUFMSJEyeeqRyqpaUl0dHRj91mbGyMs7MzCxcu5PTp0yil\niI+PZ+PGjZQqVUorH/o0Fy9e5M8//6Rr165UrVpV+9e1a1eio6Nz1G7Qh06no2PHjsyZM4cOHTpo\nH/KJiYmYmppibGzM3bt3WbhwIcAj/VG5cmXKlSvH999/T0ZGBt999x0RERFavzRr1gw/Pz9SUlKI\ni4tj1KhRfPLJJ0+M5/bt21SvXv25zkXkH0ke4oVWt25dli1bhp+fH82aNWPy5MlMnTqV1q1b065d\nO9q3b4+7uztt2rRhx44ddOnShcuXLz+13datW2v1FB7Hx8cHR0dHJk6cSNOmTXFwcODYsWNs3Ljx\nmaeXtm7dSqtWrXJMnUHW9JyTkxNffvnlM7XzOJ6enty6dSvHiGbUqFH8888/NG/eHE9PT+rUqUPt\n2rUf6Q9TU1NmzZrFl19+SfPmzTl+/LhWphWyRjCxsbE4ODjg6uqKhYUFs2bNemIsp0+ffmrNFVHw\nZEl2IfJJx44dmT9/vlZwx8HBgY0bN1KrVq1CjqzoOnbsGCtXriQwMBCAu3fv0rFjR3788UfKly9f\nyNGJh8nIQ4h8Mnz48P/07V/A9u3b6dWrlySOIkiShxD5pFOnTiQkJDz1j/bE48XHx7N//36GDBlS\n2KGIx5BpKyGEEHqTkYcQzyg9PZ3IyMjCDsMg5WXf3bhxI0/ayW9KKW7evFnYYeQbSR6iSPvggw/Y\nunVrYYcBwLhx4wgJCSnsMJ7LTz/9hIODwzO99kmr+f4XedV3mzZtws/PLw8iyh8Pv18XLVr01KVi\nnmThwoVMmTIlL0PLc/JHgqJI+/zzzws7BM3du3cLOwSDlVd9V9R/Bg+/X+/evUvFihULMZr8JSOP\nYiglJYXZs2fj7OyMlZUVLi4u2rc+Ly+vHHcA3bhxg8aNGxMfH09KSgpz587FxsaGdu3asXDhQh48\neADAihUrGDx4MB07duSdd94hMTGRXbt20bVrV5o3b06LFi2YOXOmtjzHpUuX8PLyomnTpnh7ezN9\n+nTtm1RGRgYrV67EwcGB1q1bM3XqVBITEx97Lt7e3tq3NwcHBzZs2ICLiwtWVlbMnDmTsLAwnJ2d\nadasGfPnz9f2a9iwIQEBAbRp04aWLVuydOlS7S++b9y4wZAhQ7C1taVx48Z4eXlpf6uQmZnJypUr\nsbGxwdrammHDhnH37l3mzZvHiRMn8PX1xdfX95E4k5KS8PHxoW3btrRt25Zp06aRkJCg9d2ECRMY\nPHgwTZo0oWPHjhw6dOix5ztlyhQWLVqEl5cXVlZW9O3bl9OnT+Pl5UWTJk0YOHCg1lcxMTGMHz+e\nli1bYmtry6JFi7SfV2pqKtOnT6dZs2ba35A8LLeVhHOzYcMGHB0dad68OQMHDuTKlStA1i22/15o\nMbvuyL/77tixY3Tq1InZs2fTpEkTHB0dtbW0sn92D498Ro0axYoVK/jxxx9Zs2YNISEhdO/e/ZHY\nvv32W4YMGcKUKVNo0qQJLi4u/Prrr4wfP54mTZrQqVMn7eaF3H5HADZv3oytrS1t2rTBz88vRx82\nbNiQjRs3Ym9vT4sWLZgwYYLW79nv1/Xr17Njxw4CAwMZNWrUU1caDg8Pp3///jRp0gQvLy9u3bqV\n49y++uorXFxcaNmyJcOHD3/iH6AWKCWKnZUrV6q+ffuq+Ph4lZ6erj777DP1zjvvKKWU+uqrr1Tv\n3r21165atUqNHDlSKaXU7Nmz1YABA9SdO3dUbGys6tu3r1q+fLlSSil/f3/VuHFjdeHCBRUfH69u\n3LihrKys1B9//KGUUurixYuqSZMm6vDhw+rBgwfK0dFR+fv7q9TUVHXo0CH1xhtvqMmTJyullFq7\ndq3q3LmzioiIUAkJCWrMmDFq0qRJjz2Xvn37qsDAQKWUUvb29srLy0vFxcWpS5cuqddee0317dtX\n3bt3T507d069/vrr6u+//1ZKKdWgQQPVu3dvFRsbq65fv67s7e3VV199pZRSql+/fmrhwoUqLS1N\nJSUlqSFDhqgJEyZo/ePo6KguX76sUlNT1bhx49S4ceMeieXfxo0bp/r166diY2NVXFyc+vDDD9WY\nMWO0vnvjjTfU4cOHVWpqqvL19VUuLi6PbWfy5MmqZcuW6uLFiyoxMVG5urqqtm3bqkuXLqm4uDjl\n4uKiNm3apJRSqlevXmrcuHEqISFBRUZGqm7duik/Pz+llFK+vr6qV69eKjY2Vt26dUu5ubkpe3t7\npZRSN2/eVE2aNFH79u1TaWlpavfu3apFixbq7t27Wt9duHDhkdi2bNmibGxs1Llz51RqaqpasWKF\ncnBwUPfv31dHjx5VLVq0yPH6Fi1aqKNHjz7Sd0ePHlUNGjRQc+fOVampqergwYPqzTffzPGze/j4\nI0eOVP7+/lpfZr9f/+2bb75RDRo0ULt27VIZGRlqwoQJ6vXXX1d79+5Vqampavz48dq+uf2OHD58\nWFlbW6s//vhDpaSkqJkzZ6oGDRpo59KgQQM1ZMgQlZCQoK5cuaJatmypduzY8ch5Tp48Wfn6+iql\nlLpx44Zq0KCBSkxM1OLt0qWL+uabb5RSSnXt2lX5+Pio1NRU9dtvvykrKyvt92X37t3K1tZW/f33\n3yolJUUtWLBA9enT57F9UJBk5FEM9enTB39/f8zMzLh16xZlypTRVoLt2LEjZ86c0S5e7tq1Cw8P\nD5RSfPvtt0yYMIGKFStSqVIlRo4cyddff621+9prr9GgQQPKlSuHhYUFO3bsoHHjxty9e5e4uDjM\nzc2Jiori1KlTJCQkMGzYMExNTWnbti0uLi5aO9u3b2fEiBFYWlpStmxZJkyYwA8//PBMFeN69uyJ\nubk5r7zyClWrVqV79+6UL1+eRo0aUbVqVW0ZDIDx48dTqVIlateuTb9+/bRvt76+vowaNYqMjAwi\nIiJyrJS7a9cuvL29qVevHqampkybNu2pt4qmpKTw448/MnHiRCpVqoS5uTmTJ09mz549Wu0PKysr\nWrdujampKe7u7ly/fv2J7dnb2/Pqq69SpkwZ3nrrLWxtbXnllVcwNzfn7bff5ubNm/zzzz/8/vvv\nTJs2jbJly1KtWjVGjx6trTW1Z88eBg0aRKVKlahevTqDBg3S2s9tJeHcfP/997z33ns0atQIU1NT\nhg0bxoMHD55rxVszMzMmTJiAqakp7dq1w8bGJk+WXa9ZsyYdO3bEyMiIFi1aUKNGDVxdXTE1NaVV\nq1ba+yO335EffvgBT09PGjduTMmSJZk8eTI6Xc4Z/v79+1O2bFlefvllmjRpoq2m/Dxu3LjBmTNn\nGDduHKampjRt2pROnTpp27dv3857771H/fr1KVmyJOPGjeOPP/7g6tWrz33MvCDXPIqhhIQEfHx8\nOH36NC+99BIvvfSSNp1kbm6OnZ0du3fvpnXr1sTExPDOO+9w584dUlJS8Pb21hYUVEqRlpamfag/\nvEqsTqdj27ZtbN++HTMzM15//XXS0tLIzMzk9u3bWFhYaAvkAdSoUYOYmBgga2XVSZMm5diu0+mI\niIjg5ZdfzvXcHl6O3NjYOMcfjxkZGeVYjLBOnTra/6tXr64N9a9cuYKfnx9RUVG8+uqrlChRQuuf\nmJiYHOsoVapU6ZHlP/4tPj6etLQ0atSooT1Xs2ZNlFLcvn1ba+fhc1W53CH/tHNUShEbG4uZmVmO\ndrP7OC0tjZiYmByFpbKLTgG5riScm9jY2BznaGRkhKWlJVFRUdSuXTvXff+tevXqlCxZMsfj7PfH\nf/Hw6rzGxsbaSsrZ8Wa/P3L7Hbl9+zb169fX9jMzM3tk1d+H+93ExCTXn+fTREdHY2ZmRtmyZbXn\natasqX3BuHXrFsuWLWPlypXa9hIlSjzT70t+kuRRDM2aNYtXXnmF1atXo9Pp+PXXX3N8q+vcuTMB\nAQHExcXRqVMnTExMqFChAiYmJgQFBfHSSy8BkJycTExMjPZL/vAqtbt27WL37t0EBQVpScXR0RHI\n+iC4ffs2GRkZWoKIjIzUvr1VrVqVOXPmaMuRp6WlcePGjWf6AHqWlXKz3b59mypVqgBZH5iWlpY8\nePCAESNGsGDBAtq3bw/AypUrtfnsatWq5ajXcePGDb7//nttWfLHqVKlCqampty8eVP7UAkPD8fI\nyOi5Lpg+yznWqFGD5ORk7ty5k+OY2T9HCwsLIiIiePPNNwFynFNuKwk/7ZgP33qavaR95cqVMTY2\nzrGKblpaWq4r5cbGxuZ4f0RERGj1T4yMjHK0lR8XyXP7HbG0tMwxgs1ewPG/yG2lYQsLC5KTk3Nc\nYP/3z2vgwIE5rvNcvnxZ+z0tLDJtVQwlJiZSqlQpjI2NuXXrFsuXLwf+741ra2tLeHg4QUFB2sJ3\nxsbGuLu7s3jxYuLj40lOTmbmzJlPvF0wMTERnU6HqakpDx48YO3atYSHh5Oeno6VlRUVK1bks88+\nIy0tjV9//ZXg4GBtX09PT1atWsXt27dJS0tj2bJlfPDBB//p29vj+Pv7k5iYyNWrVwkMDMTT01Mb\nSZUuXRqAU6dOsWXLFq1v3N3d2bRpE//88w+pqan4+/trUxKmpqaPvbBvZGRE586dWbx4MXfu3OHe\nvXssWrQIW1vbfFtWo1q1arRu3Zp58+aRlJREVFQU/v7+uLu7A1lfED799FOioqKIjo5m7dq12r65\nrSScG09PTzZs2MCFCxd48OCBVlu8VatW1K5dm/v377Nv3z7S09NZu3ZtjtV2/9139+7dIyAggLS0\nNMLCwjh69Kg2VVO3bhEawsEAAAMzSURBVF127txJWloav/zyS44FJp/0M9BXbr8jnp6efP/99/z5\n5588ePCATz755JlWUv63h2PNbaXhWrVq0axZMxYuXEhKSgqnT5/OUSa5S5curF+/nuvXr5OZmUlg\nYCA9e/bUpkQLiySPYmjq1KmEhoZqdbVtbW0xMzPT7igyMTGhQ4cOlC5dmrffflvbb9q0aVSsWJFO\nnTpha2tLYmLiE5fK7tKlC/Xr18fe3h47OzvOnDmDs7Mzly9fxtjYmGXLlvHTTz/RokULVq1aRcuW\nLTExMQFg8ODBNGvWjF69etGqVStOnz5NQEDAI/PK/1WtWrXo1KkT3t7evPvuu3h6elKmTBl8fHy0\nO5F8fHzw8vLi+vXrpKen061bN7p168Z7772HjY0N6enpzJw5EwA3NzfWrFnDjBkzHtvndevWpXPn\nzjg5OVGxYsUc3+zzw+LFi8nMzMTR0REPDw+aNWvGxIkTgax1taytrXFzc6Nbt245VqXNbSXh3Hh4\neDBw4ECGDx9Oy5YtOX78OOvXr8fMzAwLCwsmTpzIvHnzaNOmDYmJibz++uvavv/uu/LlyxMZGand\n1bd8+XJtmnHGjBkcOnSIFi1asGnTJtzc3LR27Ozs+Pvvv3F1df1PfZfb74i1tTUjR45kyJAh2NnZ\nUapUKXQ6nfb+fVaurq78+OOPvP/++09daXjZsmXExsbSunVrpk+fnqNYloeHBz169GDQoEFYW1vz\n/fffs2bNmv/X3h3iOghEURg+CYEVYKlsBQbTKlZQwTZa1QCKLaARdB81WCS2IcGxBCQITMVLSJ56\nnYSUJu//5Ji57mTuTOb+am9uge9JsLppmtS2rY7H47IWx7F2u53SNP1IDYfDQY/HQ/v9/iP74X1N\n0+h2u33tdMC+72Xb9tIWmqZJQRCoqqpN7xi+DScPrM6yLF0ul2UY0fP5VF3XCsNw28KAN3Rdp+v1\nqmEYNM+z7ve7PM9bpkniBxfmWJ3jOCqKQnmeK0kSua6rLMt0Op22Lg340/l8Vtd1iqJI4zjK932V\nZWn0WOM/oG0FADBG2woAYIzwAAAYIzwAAMYIDwCAMcIDAGCM8AAAGHsBcpQeUxXzLOoAAAAASUVO\nRK5CYII=\n",
                        "text/plain": [
                            "<Figure size 360x288 with 1 Axes>"
                        ]
                    },
                    "metadata": {},
                    "output_type": "display_data"
                }
            ],
            "source": [
                "plt.figure(figsize=(5,4))\n",
                "shap.summary_plot(shap_values, Xdf, plot_type='bar', max_display=10, show=False, auto_size_plot=False)\n",
                "plt.xlabel(\"mean(|SHAP value|)\\n average impact on model output magnitude\")\n",
                "plt.savefig(\"NLSYM_shap_summary_bar.pdf\", dpi=300, bbox_inches='tight')\n",
                "plt.show()"
            ]
        },
        {
            "cell_type": "code",
            "execution_count": null,
            "metadata": {
                "collapsed": true
            },
            "outputs": [],
            "source": []
        }
    ],
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3",
            "language": "python",
            "name": "python3"
        },
        "language_info": {
            "codemirror_mode": {
                "name": "ipython",
                "version": 3
            },
            "file_extension": ".py",
            "mimetype": "text/x-python",
            "name": "python",
            "nbconvert_exporter": "python",
            "pygments_lexer": "ipython3",
            "version": "3.6.5"
        }
    },
    "nbformat": 4,
    "nbformat_minor": 2
}